Spatiotemporal energy modeling for foreground segmentation in multiple object tracking

In this paper, we introduce spatiotemporal energy modeling for foreground segmentation in multiple object tracking, a high accuracy and real-time foreground target extraction algorithm. From a single video sequence with multiple moving objects and stationary background, our algorithm combines spatial (color distribution) and temporal (variety between two consecutive frames) information to extract foreground objects accurately and efficiently. The key idea of our method is to employ tracking results as feedback cues for target detection in the next frame, which adaptively updates the weights and threshold. Using spatiotemporal energy modeling, the foreground extraction errors caused by ambiguous colors in foreground and background boundary and abnormal movements can be substantially reduced. Experimental results of complex scenario video demonstrate the effectiveness of our algorithm.