Enhanced Detection Reliability for Human Tracking Based Video Analytics

Human tracking based video analytics has become a popular approach to develop smart surveillance systems, with particular interest in anomaly detection and healthcare systems. Handling inaccurate detections plays a key element in the tracking pipeline. To achieve this, modern human tracking methods have often relied on setting up a threshold on confidence scores, but missing in-depth analysis between these scores and true human detections. This may render an undesirable selection performance. For this purpose, we firstly analyze the misalignment between the given confidence scores and true human detections in the MOT16 Challenge benchmark [1]. Then we propose a global-to-local enhanced confidence rescoring strategy by exploiting the classification power of a mask region-convolutional neural network (Mask R-CNN) [2], in order to mitigate the misalignment issue. Moreover, we devise an improved pruning algorithm namely Soft-aggregated non-maximal suppression (Soft-ANMS) to further enhance the detection reliability. The proposed method can be applied as a generic early processing step to any online tracking methods for robust measurement selection. Experimental results on both tasks of measurement selection and human tracking confirm the effectiveness of the proposed method.