Robust pedestrian detection and tracking with shadow removal in indoor environments

The shadows of pedestrians decrease the tracking performance dramatically in video surveillance. This paper presents a method of shadow removal to improve the accuracy of pedestrian detection and tracking in indoor environments. The proposed method can be divided into four steps: build a background model which can be automatically updated, extract moving objects region, eliminate moving objects shadows, classify and track pedestrians in moving objects region from which shadows have been eliminated. In this work, we propose a methodology using the foreground frames without shadows to detect and track the pedestrians across training datasets. Experimental results show that our approach is capable of dealing with shadows and detecting moving pedestrians in cluttered environment. It indicates that this proposal can improve the performance of indoor pedestrians tracking.