The disclosure discloses a visual multi-object tracking based on multi-Bernoulli filter with YOLOv3 detection, belonging to the fields of machine vision and intelligent information processing. The disclosure introduces a YOLOv3 detection technology under a multiple Bernoulli filtering framework. Objects are described by using anti-interference convolution features, and detection results and tracking results are interactively fused to realize accurate estimation of video multi-object states with unknown and time-varying number. In a tracking process, matched detection boxes are combined with object tracks and object templates to determine new objects and re-recognize occluded objects in real time. Meanwhile, under the consideration of identity information of detected objects and estimated objects, identity recognition and track tracking of the objects are realized, so that the tracking accuracy of the occluded objects can be effectively improved, and track fragments are reduced. Experiments show that the disclosure has good tracking effect and robustness, and can widely meet the actual design requirements of systems such as intelligent video monitoring, human-machine interaction and intelligent traffic control.
Visual multi-object tracking based on multi-Bernoulli filter with YOLOv3 detection