Attention-Prediction Guided Motion Detection for Low-Contrast Small Targets

Small target motion detection within complex natural environment is an extremely challenging task for autonomous robots. Surprisingly, visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets are as small as a few pixels in visual field. The excellent sensitivity to small target motion relies on a class of specialized neurons called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environment where small targets always exhibit extremely low contrast to neighboring backgrounds. In this paper, we propose an attention and prediction guided visual system to overcome this limitation. The proposed visual system mainly consists of three subsystems, including an attention module, a STMD-based neural network, and a prediction module. The attention module searches for potential small targets in the predicted areas of input image and enhances their contrast to complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture allowing information processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environment.