call the restart function and handle errors better in the detection process

README.md

@@ -16,16 +16,6 @@ You see multiple bounding boxes because it draws bounding boxes from all frames
 [![](http://img.youtube.com/vi/nqHbCtyo4dY/0.jpg)](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
 
 ## Getting Started
-Build the container with
-```
-docker build -t frigate .
-```
-
-Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
-- CPU Model: `/cpu_model.tflite`
-- EdgeTPU Model: `/edgetpu_model.tflite`
-- Labels: `/labelmap.txt`
-
 Run the container with
 ```bash
 docker run --rm \
@@ -36,7 +26,7 @@ docker run --rm \
 -v /etc/localtime:/etc/localtime:ro \
 -p 5000:5000 \
 -e FRIGATE_RTSP_PASSWORD='password' \
-frigate:latest
+blakeblackshear/frigate:stable
 ```
 
 Example docker-compose:
@@ -46,7 +36,7 @@ Example docker-compose:
     restart: unless-stopped
     privileged: true
     shm_size: '1g' # should work for 5-7 cameras
-    image: frigate:latest
+    image: blakeblackshear/frigate:stable
     volumes:
       - /dev/bus/usb:/dev/bus/usb
       - /etc/localtime:/etc/localtime:ro
@@ -127,6 +117,11 @@ sensor:
         value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["inference_speed"] }}' 
         unit_of_measurement: 'ms'
 ```
+## Using a custom model
+Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
+- CPU Model: `/cpu_model.tflite`
+- EdgeTPU Model: `/edgetpu_model.tflite`
+- Labels: `/labelmap.txt`
 
 ## Tips
 - Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed

benchmark.py

@@ -1,18 +1,79 @@
-import statistics
+import os
+from statistics import mean
+import multiprocessing as mp
 import numpy as np
-import time
+import datetime
-from frigate.edgetpu import ObjectDetector
+from frigate.edgetpu import ObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
 
-object_detector = ObjectDetector()
+my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
+labels = load_labels('/labelmap.txt')
 
-frame = np.zeros((300,300,3), np.uint8)
+######
-input_frame = np.expand_dims(frame, axis=0)
+# Minimal same process runner
+######
+# object_detector = ObjectDetector()
+# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
 
-detection_times = []
+# start = datetime.datetime.now().timestamp()
 
-for x in range(0, 100):
+# frame_times = []
-    start = time.monotonic()
+# for x in range(0, 1000):
-    object_detector.detect_raw(input_frame)
+#   start_frame = datetime.datetime.now().timestamp()
-    detection_times.append(time.monotonic()-start)
 
-print(f"Average inference time: {statistics.mean(detection_times)*1000:.2f}ms")
+#   tensor_input[:] = my_frame
+#   detections = object_detector.detect_raw(tensor_input)
+#   parsed_detections = []
+#   for d in detections:
+#       if d[1] < 0.4:
+#           break
+#       parsed_detections.append((
+#           labels[int(d[0])],
+#           float(d[1]),
+#           (d[2], d[3], d[4], d[5])
+#       ))
+#   frame_times.append(datetime.datetime.now().timestamp()-start_frame)
+
+# duration = datetime.datetime.now().timestamp()-start
+# print(f"Processed for {duration:.2f} seconds.")
+# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+
+######
+# Separate process runner
+######
+def start(id, num_detections, detection_queue):
+  object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue)
+  start = datetime.datetime.now().timestamp()
+
+  frame_times = []
+  for x in range(0, num_detections):
+    start_frame = datetime.datetime.now().timestamp()
+    detections = object_detector.detect(my_frame)
+    frame_times.append(datetime.datetime.now().timestamp()-start_frame)
+
+  duration = datetime.datetime.now().timestamp()-start
+  print(f"{id} - Processed for {duration:.2f} seconds.")
+  print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+
+edgetpu_process = EdgeTPUProcess()
+
+# start(1, 1000, edgetpu_process.detect_lock, edgetpu_process.detect_ready, edgetpu_process.frame_ready)
+
+####
+# Multiple camera processes
+####
+camera_processes = []
+for x in range(0, 10):
+  camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue))
+  camera_process.daemon = True
+  camera_processes.append(camera_process)
+
+start = datetime.datetime.now().timestamp()
+
+for p in camera_processes:
+  p.start()
+
+for p in camera_processes:
+  p.join()
+
+duration = datetime.datetime.now().timestamp()-start
+print(f"Total - Processed for {duration:.2f} seconds.")

config/config.example.yml

@@ -3,9 +3,13 @@ web_port: 5000
 mqtt:
   host: mqtt.server.com
   topic_prefix: frigate
-#  client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
+  # client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
-#  user: username # Optional -- Uncomment for use
+  # user: username # Optional
-#  password: password # Optional -- Uncomment for use
+  #################
+  ## Environment variables that begin with 'FRIGATE_' may be referenced in {}.
+  ##   password: '{FRIGATE_MQTT_PASSWORD}'
+  #################
+  # password: password # Optional
 
 #################
 # Default ffmpeg args. Optional and can be overwritten per camera.

detect_objects.py

@@ -1,3 +1,4 @@
+import os
 import cv2
 import time
 import datetime
@@ -16,6 +17,8 @@ from frigate.object_processing import TrackedObjectProcessor
 from frigate.util import EventsPerSecond
 from frigate.edgetpu import EdgeTPUProcess
 
+FRIGATE_VARS = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
+
 with open('/config/config.yml') as f:
     CONFIG = yaml.safe_load(f)
 
@@ -24,6 +27,8 @@ MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
 MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
 MQTT_USER = CONFIG.get('mqtt', {}).get('user')
 MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
+if not MQTT_PASS is None:
+    MQTT_PASS = MQTT_PASS.format(**FRIGATE_VARS)
 MQTT_CLIENT_ID = CONFIG.get('mqtt', {}).get('client_id', 'frigate')
 
 # Set the default FFmpeg config
@@ -31,7 +36,7 @@ FFMPEG_CONFIG = CONFIG.get('ffmpeg', {})
 FFMPEG_DEFAULT_CONFIG = {
     'global_args': FFMPEG_CONFIG.get('global_args', 
         ['-hide_banner','-loglevel','panic']),
-    'hwaccel_args': FFMPEG_CONFIG.get('hwaccel_args', 
+    'hwaccel_args': FFMPEG_CONFIG.get('hwaccel_args',
         []),
     'input_args': FFMPEG_CONFIG.get('input_args',
         ['-avoid_negative_ts', 'make_zero',
@@ -66,26 +71,23 @@ class CameraWatchdog(threading.Thread):
         time.sleep(10)
         while True:
             # wait a bit before checking
-            time.sleep(10)
+            time.sleep(30)
+
+            if (self.tflite_process.detection_start.value > 0.0 and 
+                datetime.datetime.now().timestamp() - self.tflite_process.detection_start.value > 10):
+                print("Detection appears to be stuck. Restarting detection process")
+                self.tflite_process.start_or_restart()
+                time.sleep(30)
 
             for name, camera_process in self.camera_processes.items():
                 process = camera_process['process']
-                if (datetime.datetime.now().timestamp() - self.object_processor.get_current_frame_time(name)) > 30:
-                    print(f"Last frame for {name} is more than 30 seconds old...")
-                    if process.is_alive():
-                        process.terminate()
-                        print("Waiting for process to exit gracefully...")
-                        process.join(timeout=30)
-                        if process.exitcode is None:
-                            print("Process didnt exit. Force killing...")
-                            process.kill()
-                            process.join()
                 if not process.is_alive():
                     print(f"Process for {name} is not alive. Starting again...")
                     camera_process['fps'].value = float(self.config[name]['fps'])
                     camera_process['skipped_fps'].value = 0.0
+                    camera_process['detection_fps'].value = 0.0
                     process = mp.Process(target=track_camera, args=(name, self.config[name], FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
-                        self.tflite_process.detect_lock, self.tflite_process.detect_ready, self.tflite_process.frame_ready, self.tracked_objects_queue, 
+                        self.tflite_process.detection_queue, self.tracked_objects_queue, 
                         camera_process['fps'], camera_process['skipped_fps'], camera_process['detection_fps']))
                     process.daemon = True
                     camera_process['process'] = process
@@ -117,7 +119,7 @@ def main():
 
     # start plasma store
     plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
-    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
+    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL)
     time.sleep(1)
     rc = plasma_process.poll()
     if rc is not None:
@@ -144,11 +146,10 @@ def main():
         camera_processes[name] = {
             'fps': mp.Value('d', float(config['fps'])),
             'skipped_fps': mp.Value('d', 0.0),
-            'detection_fps': mp.Value('d', 0.0),
+            'detection_fps': mp.Value('d', 0.0)
-            'last_frame': datetime.datetime.now().timestamp()
         }
         camera_process = mp.Process(target=track_camera, args=(name, config, FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
-            tflite_process.detect_lock, tflite_process.detect_ready, tflite_process.frame_ready, tracked_objects_queue, 
+            tflite_process.detection_queue, tracked_objects_queue, 
             camera_processes[name]['fps'], camera_processes[name]['skipped_fps'], camera_processes[name]['detection_fps']))
         camera_process.daemon = True
         camera_processes[name]['process'] = camera_process
@@ -182,16 +183,23 @@ def main():
         for name, camera_stats in camera_processes.items():
             total_detection_fps += camera_stats['detection_fps'].value
             stats[name] = {
-                'fps': camera_stats['fps'].value,
+                'fps': round(camera_stats['fps'].value, 2),
-                'skipped_fps': camera_stats['skipped_fps'].value,
+                'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
-                'detection_fps': camera_stats['detection_fps'].value
+                'detection_fps': round(camera_stats['detection_fps'].value, 2)
             }
         
         stats['coral'] = {
-            'fps': total_detection_fps,
+            'fps': round(total_detection_fps, 2),
-            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2)
+            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
+            'detection_queue': tflite_process.detection_queue.qsize(),
+            'detection_start': tflite_process.detection_start.value
         }
 
+        rc = plasma_process.poll()
+        stats['plasma_store_rc'] = rc
+
+        stats['tracked_objects_queue'] = tracked_objects_queue.qsize()
+
         return jsonify(stats)
 
     @app.route('/<camera_name>/<label>/best.jpg')

frigate/edgetpu.py

@@ -1,8 +1,10 @@
 import os
 import datetime
+import hashlib
 import multiprocessing as mp
 import numpy as np
 import SharedArray as sa
+import pyarrow.plasma as plasma
 import tflite_runtime.interpreter as tflite
 from tflite_runtime.interpreter import load_delegate
 from frigate.util import EventsPerSecond
@@ -60,77 +62,78 @@ class ObjectDetector():
         
         return detections
 
+def run_detector(detection_queue, avg_speed, start):
+    print(f"Starting detection process: {os.getpid()}")
+    plasma_client = plasma.connect("/tmp/plasma")
+    object_detector = ObjectDetector()
+
+    while True:
+        object_id_str = detection_queue.get()
+        object_id_hash = hashlib.sha1(str.encode(object_id_str))
+        object_id = plasma.ObjectID(object_id_hash.digest())
+        object_id_out = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{object_id_str}")).digest())
+        input_frame = plasma_client.get(object_id, timeout_ms=0)
+
+        if input_frame is plasma.ObjectNotAvailable:
+            plasma_client.put(np.zeros((20,6), np.float32), object_id_out)
+            continue
+
+        # detect and put the output in the plasma store
+        start.value = datetime.datetime.now().timestamp()
+        plasma_client.put(object_detector.detect_raw(input_frame), object_id_out)
+        duration = datetime.datetime.now().timestamp()-start.value
+        start.value = 0.0
+
+        avg_speed.value = (avg_speed.value*9 + duration)/10
+        
 class EdgeTPUProcess():
     def __init__(self):
-        # TODO: see if we can use the plasma store with a queue and maintain the same speeds
+        self.detection_queue = mp.Queue()
-        try:
-            sa.delete("frame")
-        except:
-            pass
-        try:
-            sa.delete("detections")
-        except:
-            pass
-
-        self.input_frame = sa.create("frame", shape=(1,300,300,3), dtype=np.uint8)
-        self.detections = sa.create("detections", shape=(20,6), dtype=np.float32)
-
-        self.detect_lock = mp.Lock()
-        self.detect_ready = mp.Event()
-        self.frame_ready = mp.Event()
         self.avg_inference_speed = mp.Value('d', 0.01)
+        self.detection_start = mp.Value('d', 0.0)
+        self.detect_process = None
+        self.start_or_restart()
 
-        def run_detector(detect_ready, frame_ready, avg_speed):
+    def start_or_restart(self):
-            print(f"Starting detection process: {os.getpid()}")
+        self.detection_start.value = 0.0
-            object_detector = ObjectDetector()
+        if (not self.detect_process is None) and self.detect_process.is_alive():
-            input_frame = sa.attach("frame")
+            self.detect_process.terminate()
-            detections = sa.attach("detections")
+            print("Waiting for detection process to exit gracefully...")
-
+            self.detect_process.join(timeout=30)
-            while True:
+            if self.detect_process.exitcode is None:
-                # wait until a frame is ready
+                print("Detection process didnt exit. Force killing...")
-                frame_ready.wait()
+                self.detect_process.kill()
-                start = datetime.datetime.now().timestamp()
+                self.detect_process.join()
-                # signal that the process is busy
+        self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.avg_inference_speed, self.detection_start))
-                frame_ready.clear()
-                detections[:] = object_detector.detect_raw(input_frame)
-                # signal that the process is ready to detect
-                detect_ready.set()
-                duration = datetime.datetime.now().timestamp()-start
-                avg_speed.value = (avg_speed.value*9 + duration)/10
-
-        self.detect_process = mp.Process(target=run_detector, args=(self.detect_ready, self.frame_ready, self.avg_inference_speed))
         self.detect_process.daemon = True
         self.detect_process.start()
 
 class RemoteObjectDetector():
-    def __init__(self, labels, detect_lock, detect_ready, frame_ready):
+    def __init__(self, name, labels, detection_queue):
         self.labels = load_labels(labels)
-
+        self.name = name
-        self.input_frame = sa.attach("frame")
-        self.detections = sa.attach("detections")
-
         self.fps = EventsPerSecond()
-
+        self.plasma_client = plasma.connect("/tmp/plasma")
-        self.detect_lock = detect_lock
+        self.detection_queue = detection_queue
-        self.detect_ready = detect_ready
-        self.frame_ready = frame_ready
     
     def detect(self, tensor_input, threshold=.4):
         detections = []
-        with self.detect_lock:
+
-            self.input_frame[:] = tensor_input
+        now = f"{self.name}-{str(datetime.datetime.now().timestamp())}"
-            # unset detections and signal that a frame is ready
+        object_id_frame = plasma.ObjectID(hashlib.sha1(str.encode(now)).digest())
-            self.detect_ready.clear()
+        object_id_detections = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{now}")).digest())
-            self.frame_ready.set()
+        self.plasma_client.put(tensor_input, object_id_frame)
-            # wait until the detection process is finished,
+        self.detection_queue.put(now)
-            self.detect_ready.wait()
+        raw_detections = self.plasma_client.get(object_id_detections)
-            for d in self.detections:
+
-                if d[1] < threshold:
+        for d in raw_detections:
-                    break
+            if d[1] < threshold:
-                detections.append((
+                break
-                    self.labels[int(d[0])],
+            detections.append((
-                    float(d[1]),
+                self.labels[int(d[0])],
-                    (d[2], d[3], d[4], d[5])
+                float(d[1]),
-                ))
+                (d[2], d[3], d[4], d[5])
+            ))
+        self.plasma_client.delete([object_id_frame, object_id_detections])
         self.fps.update()
         return detections

frigate/object_processing.py

@@ -34,7 +34,8 @@ class TrackedObjectProcessor(threading.Thread):
             'best_objects': {},
             'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
             'tracked_objects': {},
-            'current_frame_time': datetime.datetime.now().timestamp()
+            'current_frame': np.zeros((720,1280,3), np.uint8),
+            'object_id': None
         })
         
     def get_best(self, camera, label):
@@ -45,9 +46,6 @@ class TrackedObjectProcessor(threading.Thread):
     
     def get_current_frame(self, camera):
         return self.camera_data[camera]['current_frame']
-    
-    def get_current_frame_time(self, camera):
-        return self.camera_data[camera]['current_frame_time']
 
     def run(self):
         while True:
@@ -64,33 +62,39 @@ class TrackedObjectProcessor(threading.Thread):
             object_id_hash = hashlib.sha1(str.encode(f"{camera}{frame_time}"))
             object_id_bytes = object_id_hash.digest()
             object_id = plasma.ObjectID(object_id_bytes)
-            current_frame = self.plasma_client.get(object_id)
+            current_frame = self.plasma_client.get(object_id, timeout_ms=0)
-
-            # draw the bounding boxes on the frame
-            for obj in tracked_objects.values():
-                thickness = 2
-                color = COLOR_MAP[obj['label']]
-                
-                if obj['frame_time'] != frame_time:
-                    thickness = 1
-                    color = (255,0,0)
 
+            if not current_frame is plasma.ObjectNotAvailable:
                 # draw the bounding boxes on the frame
-                box = obj['box']
+                for obj in tracked_objects.values():
-                draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
+                    thickness = 2
-                # draw the regions on the frame
+                    color = COLOR_MAP[obj['label']]
-                region = obj['region']
+                    
-                cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
+                    if obj['frame_time'] != frame_time:
-            
+                        thickness = 1
-            if config['snapshots']['show_timestamp']:
+                        color = (255,0,0)
-                time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-                cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
 
-            ###
+                    # draw the bounding boxes on the frame
-            # Set the current frame as ready
+                    box = obj['box']
-            ###
+                    draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
-            self.camera_data[camera]['current_frame'] = current_frame
+                    # draw the regions on the frame
-            self.camera_data[camera]['current_frame_time'] = frame_time
+                    region = obj['region']
+                    cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
+                
+                if config['snapshots']['show_timestamp']:
+                    time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
+                    cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
+
+                ###
+                # Set the current frame as ready
+                ###
+                self.camera_data[camera]['current_frame'] = current_frame
+
+                # store the object id, so you can delete it at the next loop
+                previous_object_id = self.camera_data[camera]['object_id']
+                if not previous_object_id is None:
+                    self.plasma_client.delete([previous_object_id])
+                self.camera_data[camera]['object_id'] = object_id
             
             ###
             # Maintain the highest scoring recent object and frame for each label
@@ -104,10 +108,10 @@ class TrackedObjectProcessor(threading.Thread):
                     # if the object is a higher score than the current best score 
                     # or the current object is more than 1 minute old, use the new object
                     if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
-                        obj['frame'] = np.copy(current_frame)
+                        obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
                         best_objects[obj['label']] = obj
                 else:
-                    obj['frame'] = np.copy(current_frame)
+                    obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
                     best_objects[obj['label']] = obj
 
             ###

frigate/objects.py

@@ -49,14 +49,6 @@ class ObjectTracker():
             obj['history'] = [entry]
 
     def match_and_update(self, frame_time, new_objects):
-        if len(new_objects) == 0:
-            for id in list(self.tracked_objects.keys()):
-                if self.disappeared[id] >= self.max_disappeared:
-                    self.deregister(id)
-                else:
-                    self.disappeared[id] += 1
-            return
-            
         # group by name
         new_object_groups = defaultdict(lambda: [])
         for obj in new_objects:
@@ -69,6 +61,18 @@ class ObjectTracker():
                 'frame_time': frame_time
             })
         
+        # update any tracked objects with labels that are not
+        # seen in the current objects and deregister if needed
+        for obj in list(self.tracked_objects.values()):
+            if not obj['label'] in new_object_groups:
+                if self.disappeared[obj['id']] >= self.max_disappeared:
+                    self.deregister(obj['id'])
+                else:
+                    self.disappeared[obj['id']] += 1
+        
+        if len(new_objects) == 0:
+            return
+        
         # track objects for each label type
         for label, group in new_object_groups.items():
             current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]

frigate/video.py

@@ -20,7 +20,6 @@ from frigate.objects import ObjectTracker
 from frigate.edgetpu import RemoteObjectDetector
 from frigate.motion import MotionDetector
 
-# TODO: add back opencv fallback
 def get_frame_shape(source):
     ffprobe_cmd = " ".join([
         'ffprobe',
@@ -99,7 +98,23 @@ def create_tensor_input(frame, region):
     # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
-def track_camera(name, config, ffmpeg_global_config, global_objects_config, detect_lock, detect_ready, frame_ready, detected_objects_queue, fps, skipped_fps, detection_fps):
+def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
+    if not ffmpeg_process is None:
+        print("Terminating the existing ffmpeg process...")
+        ffmpeg_process.terminate()
+        try:
+            print("Waiting for ffmpeg to exit gracefully...")
+            ffmpeg_process.wait(timeout=30)
+        except sp.TimeoutExpired:
+            print("FFmpeg didnt exit. Force killing...")
+            ffmpeg_process.kill()
+            ffmpeg_process.wait()
+
+    print("Creating ffmpeg process...")
+    print(" ".join(ffmpeg_cmd))
+    return sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size*10)
+
+def track_camera(name, config, ffmpeg_global_config, global_objects_config, detection_queue, detected_objects_queue, fps, skipped_fps, detection_fps):
     print(f"Starting process for {name}: {os.getpid()}")
 
     # Merge the ffmpeg config with the global config
@@ -109,6 +124,13 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
     ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', ffmpeg_global_config['hwaccel_args'])
     ffmpeg_input_args = ffmpeg.get('input_args', ffmpeg_global_config['input_args'])
     ffmpeg_output_args = ffmpeg.get('output_args', ffmpeg_global_config['output_args'])
+    ffmpeg_cmd = (['ffmpeg'] +
+            ffmpeg_global_args +
+            ffmpeg_hwaccel_args +
+            ffmpeg_input_args +
+            ['-i', ffmpeg_input] +
+            ffmpeg_output_args +
+            ['pipe:'])
 
     # Merge the tracked object config with the global config
     camera_objects_config = config.get('objects', {})    
@@ -125,7 +147,11 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
     expected_fps = config['fps']
     take_frame = config.get('take_frame', 1)
 
-    frame_shape = get_frame_shape(ffmpeg_input)
+    if 'width' in config and 'height' in config:
+        frame_shape = (config['height'], config['width'], 3)
+    else:
+        frame_shape = get_frame_shape(ffmpeg_input)
+
     frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
 
     try:
@@ -146,21 +172,11 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
         mask[:] = 255
 
     motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
-    object_detector = RemoteObjectDetector('/labelmap.txt', detect_lock, detect_ready, frame_ready)
+    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue)
 
     object_tracker = ObjectTracker(10)
-
-    ffmpeg_cmd = (['ffmpeg'] +
-            ffmpeg_global_args +
-            ffmpeg_hwaccel_args +
-            ffmpeg_input_args +
-            ['-i', ffmpeg_input] +
-            ffmpeg_output_args +
-            ['pipe:'])
-
-    print(" ".join(ffmpeg_cmd))
     
-    ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size)
+    ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size)
     
     plasma_client = plasma.connect("/tmp/plasma")
     frame_num = 0
@@ -177,7 +193,14 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
         avg_wait = (avg_wait*99+duration)/100
 
         if not frame_bytes:
-            break
+            rc = ffmpeg_process.poll()
+            if rc is not None:
+                print(f"{name}: ffmpeg_process exited unexpectedly with {rc}")
+                ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process)
+                time.sleep(10)
+            else:
+                print(f"{name}: ffmpeg_process is still running but didnt return any bytes")
+            continue
 
         # limit frame rate
         frame_num += 1
@@ -350,3 +373,5 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
         plasma_client.put(frame, plasma.ObjectID(object_id))
         # add to the queue
         detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects))
+
+    print(f"{name}: exiting subprocess")