Local Computation with Adaptive Spatial Clustering for Multi-Size Motion Patch Proposals in WAMI

Near real-time moving object detection in Wide Area Motion Imagery (WAMI) can support applications in many fields. However, since the targets’ search space are extremely large, current state-of-the-art methods usually suffers high computational cost. It’s crucial to recommend candidate regions for detection first. Different from most research that extracts candidate blobs as proposals, this paper attempts to offer high-quality patches, which can provide background info and be taken as many algorithms’ input (convolutional neural network, optical flow, block matching, etc.). First by the idea that local errors are tolerable, neighborhood frame differencing with local computation is applied to roughly obtain irregular blobs. After that, an adaptive spatial clustering algorithm which utilizes grid and density reachable, is proposed to generate multi-size motion patches quickly. Compared with traditional clustering, advantages of this algorithm include parameter-free, saving of time and widely applications. Experimental results show that the proposed method is competitive especially in dense traffic regions.