Design of a Real-Time Movement Decomposition-Based Rodent Tracker and Behavioral Analyzer Based on FPGA

作者：Xuecheng Wang, Yahao Song, Fengfan Hou, Milin Zhang, Andrew G. Richardson, Timothy H. Lucas, Jan Van der Spiegel · 发表于：IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 年份：2022 · DOI：10.1109/tvlsi.2022.3168783 · 被引用次数：2 · 研究领域：Zebrafish Biomedical Research Applications、Human Pose and Action Recognition、Neuroscience and Neuropharmacology Research

In this work, a real-time movement decomposition-based automatic behavior analysis system is proposed. The proposed method is evaluated on a Morris water maze experiment with rat. The system integrated a high-speed tracker, a feature extraction module, and a classifier on a Xilinx Ultra-96 single computer board with a field-programmable gate array (FPGA). The high-speed tracker includes a movement predictor based on motion features and pose features, and a fast-checking algorithm to evaluate whether the tracking result is correct or not. A faster region-based convolutional neural network (Faster-RCNN)-based detector is used for initializing the tracker. The feature extraction module integrated a CNN-based feature point recognition network and a motion feature computing module. Motion and pose feature are calculated with the result of the feature points. The proposed system achieves a 66.2-frames/s processing speed, and the average point detection error is 4.01 pixels. For each video, the classification and regression tree (CART)-based classifier gives an explorative stage in five categories, which represents the rat’s strategy of exploration in Morris water maze task. This work focuses on a real-time local computing system design on small-scale datasets.