A fractional-order visual neural model for small target motion detection

Detecting small moving targets consisting of one or few pixels is technically demanding due to their limited visual features. Motivated by nature, some bio-inspired models have been developed that simulate the behavior of small target motion detectors (STMDs), a class of specialized neurons found in insects’ visual neural systems dominating in detecting small moving targets during activities such as predation or courtship. However, the existing models’ high dependence on the sampling frequency of input videos becomes a bottleneck that seriously hinders their real-time applications in the physical world. This is because massive computational power is required to capture and process high-sampling-frequency videos. While model detection performance in low-sampling-frequency videos is plagued by significant spatial errors and weak responses. To address these issues, we propose an STMD-based visual neural model with a fractional-order difference operator for small target motion detection. The fractional-order operator captures instantaneous luminance change and integrates it with memory information, where the instantaneous information dominates the integrated signal. The STMD network further separates the rising and falling luminance components, which are aligned in the time domain and then multiplied to predict the location of moving small targets. Due to the rapid response of instantaneous information and the supplement of memory information, the proposed model locates the small moving targets accurately and robustly in low-sampling-frequencies. Numerical experiments show that the proposed model significantly improves the detection performance for low-sampling-frequency videos.