Aiming at the high complexity of existing crowd abnormal detection models, the inability of traditional CNN to extract time-related features, and the lack of training samples, an improved spatial-temporal convolution neural network is proposed in this paper. The algorithm firstly uses the aggregation channel feature model to process the surveillance image, and selects the suspected object region with saliency characteristics. Then, the scaled correction and feature extraction are performed on the obtained suspected object region. The corresponding low-level features are obtained and input into the deep network for deep feature learning so as to enhance the representation ability. Finally, the deep feature is input into the least squares SVM classification model to obtain the final abnormal behavior detection result. The embedded chip Hi353I is used as the hardware processor to realize the real-time abnormal behavior detection effect. Our proposed deep intelligent analysis algorithm is used as abnormal Behavior Detector in the board level test. The results show that most of abnormal behaviors can be detected and the alarming message can be timely transmitted in the real-time surveillance.

中文翻译：

---

大规模视频监控中基于深度智能分析算法的异常行为检测设计与实现

针对现有人群异常检测模型复杂度高，传统CNN无法提取时间相关特征，训练样本不足等问题，提出一种改进的时空卷积神经网络。该算法首先使用汇聚通道特征模型对监控图像进行处理，然后选择具有显着性特征的可疑对象区域。然后，对所获得的可疑对象区域执行缩放校正和特征提取。获取对应的低层特征并将其输入到深度网络中进行深度特征学习，以增强表示能力。最后，将深度特征输入到最小二乘SVM分类模型中，以获得最终的异常行为检测结果。嵌入式芯片Hi353I作为硬件处理器，实现了实时异常行为检测效果。我们提出的深度智能分析算法被用作板级测试中的异常行为检测器。结果表明，在实时监控中，可以检测到大多数异常行为，并及时发送告警信息。