运动信息引导的目标检测算法

在室外监控视频的场景下，由于场景的复杂性及目标的多样性，监控视频中的目标存在难以检测的情况，如目标被遮挡、目标尺寸变化等，目标检测任务仍然存在挑战。基于此，提出了一种利用运动信息引导基于卷积神经网络的目标检测算法来提高目标检测的准确率。对运动目标检测算法进行一定的改进，使得到的运动前景图中能够保持静止目标前景的存在；利用运动前景图中的前景可以指示目标空间位置的特点，在特征层面将网络提取的特征图与获取的以运动前景图为主的运动信息相融合，提高特征图可能存在目标区域的响应值；在目标检测算法的检测器中，引入一个定位分支，利用视频帧的运动前景图，学习候选目标的定位置信度，并与目标的分类置信度加权求和，作为目标最终的置信度，再通过非极大值抑制方法得到检测结果。实验证明，在固定摄像机下采集的数据集中，所提算法能够提升目标检测的准确率。 

Object detection algorithm guided by motion information

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.