Trajectory Prediction Enhancement Method for Multiple Object Tracking in Complex Scenes
Taking the short-track speed skating match of the Winter Olympics as an example,a multiple object tracking dataset applicable to the short-track speed skating scene is designed based on the athletes'motion char-acteristics,such as small distinctions in target appearance,fast motion changes,and frequent mutual occlusion between targets.A multiple object tracking method based on trajectory prediction enhancement was proposed.First,the intersection-over-union of bounding boxes and the cosine distance of appearance features are calcu-lated to jointly judge the similarity between detection and trajectories,solving the problem of similar target ap-pearances.Secondly,the tracking trajectory's global features and motion clues are used to recover the lost infor-mation of occluded targets,improving the re-association ability of interrupted trajectories.Finally,the detection prior is used to control the initialization of new trajectories,reducing the impact of noisy detection on identity exchange during trajectory tracking.Experimental results show that compared with the DeepSORT method,this method can guarantee stable tracking in the short-track speed skating scene and effectively reduce trajectory in-terruptions,with an increase of 21 percentage points in IDF1 and 14.3 percentage points in multiple object track-ing accuracy(MOTA).This method ensures long-term stable tracking in short-track speed skating scenes with small differences in targets and fast motion changes.It has inspirational significance for the application of multi-ple object tracking in complex scenes.
deep learningmultiple object trackingshort-track speed skatingKalman filtertrack predict
NETL
NSTL
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