运动信息引导的目标检测算法

Due to the complexity of the scene and the diversity of objects, the objects in the scene of outdoor surveillance video are difficult to detect, which involves such problems like the object is blocked, or the size of object changes. Therefore, the object detection task is still challenging. To improve the accuracy of the object detection algorithm, this paper proposed a method of using motion information to guide the object detection algorithm based on convolutional neural network. Firstly, the motion object detection algorithm is improved to keep the foreground of stationary target in the motion foreground map; secondly, using the feature that the foreground in the motion foreground map can indicate the spatial position of the object, the feature map extracted by the network is fused with the motion information to improve the response value of the possible object area in the feature map; finally, in the detector of the object detection algorithm, a localization branch is introduced. Using the motion foreground map of the video frame, the location reliability of the candidate object is learned, and weighted sum with the classification confidence of the object is used as the final confidence of the object. The detection result is obtained through the non maximum suppression method. Experiments show that the proposed method can improve the accuracy of object detection in the data set collected under the fixed camera.