Real-time running event detection via a community patrol robot

Security surveillance is an important application for patrol robots. In this article, a real-time running event detection method is proposed for the community patrol robot. Although sliding window-based approaches have been quite successful in detecting objects in images, directly extending them to real-time object detection in video is not simple. This is due to the huge samples and diversity of object appearances with multivisual view and scale. To address these limitations, first, a simple and effective spatial-temporal filtering-based approach is proposed to obtain moving object proposals in each frame; then, two-stream convolutional networks fusion architecture is introduced to best take advantage of the spatial-temporal information from the proposal. The algorithm is applied on PatrolBot in community environments and runs at 15 fps on a consumer laptop. Two benchmark data sets (the Kungliga Tekniska Hogskolan [KTH] data set and Nanyang Technological University [NTU] running data set) were also used to compare results with previous works. Experimental results show higher accuracy and lower detection error rate in the proposed method.