Object detection method and system based on synchronized video frame reading, and device

An object detection method based on synchronized video frame reading, wherein the object detection method is applied to a monitoring system, and the monitoring system comprises a network time protocol (NTP) server and a plurality of camera devices;

• the NTP server and the plurality of camera devices are all disposed in a same local area network;
• and any one of the camera devices is configured with an NTP client, a reading thread, and an FFmpeg encoding thread;
• the NTP client is configured to generate a timestamp while a video frame is generated by a corresponding camera device;
• the NTP server is configured to synchronize timestamps generated by a plurality of NTP clients at a same moment;
• the reading thread is configured to read raw video stream data of a plurality of video frames generated by the corresponding camera device and a timestamp corresponding to each video frame;
• and
• the FFmpeg encoding thread is configured to encode, according to a time sequence, the raw video stream data of each video frame and the timestamp corresponding to each video frame to obtain a plurality of timestamped video frames, so as to form a low-latency buffer queue of the corresponding camera device;
• and
• the object detection method comprises:
• determining, according to a plurality of low-latency buffer queues, a synchronized timestamped video sequence, wherein a timestamp difference corresponding to any two timestamped video frames in the synchronized timestamped video sequence is less than a timestamp difference threshold;
• and
• inputting the synchronized timestamped video sequence into an object detection model to obtain an object detection result corresponding to a central timestamp, wherein the object detection model is obtained by training a deep learning model by using a plurality of annotated synchronized timestamped video historical sequences.