Abnormal behaviour detection algorithm needs to conduct behaviour analysis on the basis of continuous video inclination tracking, and the robustness of the algorithm is reduced for the occlusion of moving targets, the occlusion of the environment, and the movement of targets with the same colour. For this reason, the optical flow information between RGB (red, green, and blue) images and video frames is used as the input of the network in view of group behaviour. Then, the direction, velocity, acceleration, and energy of the crowd were weighted and fused into a global optical flow descriptor. At the same time, the crowd trajectory map is extracted from the original image of a single frame. Following, in order to realize the detection of large displacement moving target and solve the problem that the traditional optical flow algorithm is only suitable for the detection of displacement moving target, a video abnormal behaviour detection algorithm based on the double-flow convolutional neural network is proposed. The network uses two network branches to learn spatial dimension information and temporal dimension information, respectively, and uses short- and long-time neural network to model the dependency relationship between long-time video frames, so as to obtain the final behaviour classification results. Simulation test results show that the proposed method can achieve good recognition effect on multiple datasets, and the performance of abnormal behaviour detection can be significantly improved by using interframe motion information.

中文翻译：

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基于全局光流的群异常行为检测算法

异常行为检测算法需要在连续的视频倾斜度跟踪的基础上进行行为分析，并降低了运动目标的遮挡，环境的遮挡以及同色目标的运动的算法的鲁棒性。因此，鉴于组行为，将RGB（红色，绿色和蓝色）图像和视频帧之间的光流信息用作网络的输入。然后，对人群的方向，速度，加速度和能量进行加权，并融合到全局光流描述符中。同时，从单帧的原始图像中提取人群轨迹图。下列的，为了实现大位移目标的检测并解决传统的光流算法仅适合于位移目标的检测的问题，提出了一种基于双流卷积神经网络的视频异常行为检测算法。该网络使用两个网络分支分别学习空间维度信息和时间维度信息，并使用短期和长期神经网络对长时间视频帧之间的依赖关系进行建模，以获得最终的行为分类结果。仿真测试结果表明，该方法对多个数据集具有良好的识别效果，利用帧间运动信息可以显着提高异常行为检测的性能。