Abstract
A Samll Moving Object Detection algorithm Based on Motion Information (SMOD-BMI) was proposed to detect small moving objects with low Signal-to-Noise Ratio (SNR). Firstly, To capture suspicious moving objects, a ConvLSTM-SCM-PAN model structure was designed, in which the Convolutional Long and Short Time Memory (ConvLSTM) network fused temporal and spatial information, the Selective Concatenate Module (SCM) was selected to solve the problem of channel unbalance during feature fusion, and the Path Aggregation Network (PAN) located the suspicious moving objects. Then, an object tracking algorithm is used to track suspicious moving objects and calculate their Motion Range (MR). At the same time, according to the moving speed of the suspicious moving objects, the size of their MR is adjusted adaptively (To be specific, if the objects move slowly, we expand their MR according their speed to ensure the contextual environment information) to obtain their Adaptive Candidate Motion Range (ACMR), so as to ensure that the SNR of the moving object is improved while the necessary context information is retained adaptively. Finally, a LightWeight SCM U-Shape Net (LW-SCM-USN) based on ACMR with a SCM module is designed to classify and locate small moving objects accurately and quickly. In this paper, the moving bird in surveillance video is used as the experimental dataset to verify the performance of the algorithm. The experimental results show that the proposed small moving object detection method based on motion information can effectively reduce the missing rate and false detection rate, and its performance is better than the existing moving small object detection method of SOTA.
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URL
https://arxiv.org/abs/2301.01917
