object Tracking through Multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the Tracking process. This paper proposes a Multiple-camera-based cooperative Tracking method to overcome the occlusion problem. The paper presents a new model for combining convolutional neural networks (CNNs), which allows the proposed method to learn the features with high discriminative power and geometrical independence. In the training phase, the CNNs are first pre-trained in each of the camera views, and a convolutional gating network (CGN) is simultaneously pre-trained to produce a weight for each CNN output. The CNNs are then transferred to the Tracking task where the pre-trained parameters of the CNNs are re-trained by using the data from the Tracking phase. The weights obtained from the CGN are used in order to fuse the features learnt by the CNNs and the resulting weighted combination of the features is employed to represent the objects. Finally, the particle filter is used in order to track objects. The experimental results showed the efficiency of the proposed method in this paper.

In video surveillance, Multiple objects Tracking (MOT) is a challenging task due to object matching problem in consecutive frames. The present paper aims to propose an improved object matching approach in MOT based on Zernike Moments and combination of Multiple similarity distance metrics. In this work, the object is primarily detected using background subtraction method while the Gaussian Mixture Model (GMM) is applied for object extraction in the next frames. Subsequently, the color histogram and the magnitude of Zernike moments of the objects are calculated. In the next step, the objects are matched in the current and the previous frames based on the Hausdorff distance between objects, Earth Mover's (EMD) distance between their color histograms, and Chi-square distance between their Zernike moments. Then, a voting mechanism is designed to find the best consensus object matching from the aforementioned metrics. Eventually, the location of each object is predicted by the Kalman filter to continue Tracking in subsequent frames. The results show that the object Tracking and matching performance is improved using the proposed method in the video sequences of the Multi-camera pedestrian "EPFL" video dataset. Specifically, errors caused by the merging of targets are reduced in the proposed Tracking process.

Background and objectives: object Tracking in video streams is one of the issues in machine vision that has many applications. Depending on the type of the object, the number of objects and other inputs used in Tracking, object Tracking is divided into several different categories. Multi-object Tracking in a camera network is one of the most complex types of object Tracking. In this type of Tracking, the goal of the algorithm is to extract the persistent trace of several objects moving simultaneously in a wide area that is monitored by a network of cameras. This type of Tracking is often done in two steps. In the first step, the traces of each object in each camera is called tracklets are extracted. Then, the persistent trace of the objects are obtained by associating the extracted tracklets of all cameras in the monitored wide area. Here, we introduce a novel variational approach based on the deep features to associate the tracklets. Methods: For this purpose a variational model with Multiphase level set representation is introduced. The persistent trace of all objects are obtained by optimizing the proposed variational model. The proposed variational model is optimized by employing the Euler-Lagrange equation. CNN and deep learning are used to extract the deep features of appearance and motion of objects. Here, a ResNet50 network that is pre-trained on ImageNet and a transformer neural network which is trained with motion parameters of tracklets such as acceleration and orientation change rate are used for extracting deep features. Results: The Multiphase model using deep features presented in this paper provide 9% better results than the Multiphase model without deep features based on TCF and FS metrics and 8% better results based on MT metric. Conclusion: The results on the three well-known datasets which are real and a synthesized dataset show that the proposed model takes competitive performance, while using less extra context information of the camera network and objects, compared to the other proposed methods. The evaluations show the quality of the proposed model in solving complex problems using the minimum required initial knowledge.

This paper presents a method for Tracking an object in a sequence of images given its location in the first frame. Recently, a class of techniques called discriminative methods has shown promising results. These methods are based on training a classifier to distinguish the object from surrounding background. However, discriminative methods do not explicitly model the object. Therefore, noisy samples are likely to interfere and cause visual drift. In this paper, 3D joint RGB histograms of the object and surrounding background are used to develop an object model. An incremental color learning scheme with a forgetting factor is applied to evolve the object model during Tracking. It is shown the proposed method can handle visual drift effectively. Evaluated against five state of the art methods, experiments demonstrate superior results of the proposed Tracking algorithm. Implemented in MATLAB, the algorithm runs at 17.2 frames per second, including image input/output time.