With crimes on the rise all around the world, video surveillance is becoming
more important day by day. Due to the lack of human resources to monitor this
increasing number of cameras manually new computer vision algorithms to perform
lower and higher level tasks are being developed. We have developed a new
method incorporating the most acclaimed Histograms of Oriented Gradients the
theory of Visual Saliency and the saliency prediction model Deep Multi Level
Network to detect human beings in video sequences. Furthermore we implemented
the k Means algorithm to cluster the HOG feature vectors of the positively
detected windows and determined the path followed by a person in the video. We
achieved a detection precision of 83.11% and a recall of 41.27%. We obtained
these results 76.866 times faster than classification on normal images.

通过采用方向梯度直方图、视觉显著性和深度多层网络的显著性预测模型，结合 k 均值算法，该研究提出了一种用于视频中检测人的方法，取得了 83.11％的检测精度、41.27％的召回率的效果，并比在普通图像上对人物进行分类的速度快 76.866 倍。