Video surveillance is widely used and plays a huge role in society. Due to surveillance videos are often continuously produced, using these videos to track objects is a challenge for conventional moving object tracking methods. In this paper, in order to deal with the fast moving object and the problem of target occlusion, we propose an object tracking method based on YOLOv3 and MeanShift combined with Kalman filter aiming to improve the speed and accuracy of tracking. We use YOLOv3 to realize the detection and use the MeanShift combined with Kalman filter to track the target. The results of the experiment show that our method has achieved good results.
In this paper, we propose a model based on DenseNets and scale invariant feature transform (SIFT) keypoint match technology for object detection and segmentation. DenseNets is built on Convolutional Neural Networks (CNNs) with dense connections and used for semantic image segmentation. Our main idea is that, on the basis of the DenseNets model, we conduct the morphological processing, and apply the SIFT keypoint match technology to detect the object pixels. Opening operation and closing operation are the basic operations of morphological processing. They all consist of erosion operation and dilation operation but the order is different between them. The morphological processing combines two kinds of operations and can form a morphological filter which can filter the noise. The SIFT keypoint match algorithm is widely used to extract the invariant of position, scale and rotation so we use it to eliminate the misjudgment. Our experiments show that our method can acquire more accurate results compared with DenseNets model.