The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
We present in this paper a novel framework for multiple object tracking. The proposed algorithm consists of two main steps. The first one initializes the correspondences between detected objects and tracks by using the spatio-colorimetric model of tracks coupled with the appearance-based descriptors of objects. Two color-based models are used to enhance the robustness of the tracking result. In the...
Covariance matching techniques have recently grown in interest due to their good performances for object retrieval, detection and tracking. By mixing color and texture information in a compact representation, it can be applied to various kinds of objects (textured or not, rigid or not). Unfortunately, the original version requires heavy computations, and is difficult to execute in real-time on embedded...
This paper proposes a tracking method based on parameters obtained from multiple blobs. The multiple blobs are derived or obtained from segmenting a single blob into multiple blobs or multiple regions that have the same color information where these regions generally remain the same as the target moves. The target being tracked is a person or human walking through the camera view field where the number...
Color-invariance property of objects is effectively employed for developing non-rigid object tracking algorithms in the field of computer vision. This paper develops a novel color-based tracking algorithm for non-rigid 3-D objects with multiple colors. Especially, the proposed particle filter method can track the targets even if the appearance /disappearance of color regions were occurred by self-occlusion...
In Mean Shift algorithm, the features of the tracked target and the image matching similarity criterion have great influence on the result of tracking. a new algorithm of target tracking is proposed. the algorithm combine local binary pattern and color information to form a new feature CL, which tracks target by using a method of centroid iteration based on maximum posterior probability. Thanks to...
Human tracking is an important vision task in video surveillance and perceptual human-computer interfaces. This paper presents a novel algorithm for region-based human tracking using color and depth features. We propose an adaptive autoregressive logarithmic search (ARLS) to estimate the target position, and use depth information to further reduce the false alarm rate. The new ARLS algorithm is evaluated...
One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we present a solution that is based on our earlier approach for object tracking using particle filters and component-based descriptors. We extend the approach to deal with changing backgrounds by using a quick training phase...
This paper is dedicated to people tracking and identification in the multi-camera surveillance system. In the proposed method, each people-image is extracted among each camera and then is labeled with its color vector. Color vector provides a similar probability for each person appeared in different camera¡¦s surveillance frame. By combining the pedestrian¡¦s trajectory with relations among different...
Following people in different video sources is a challenging task: variations in the type of camera, in the lighting conditions, in the scene settings (e.g. crowd or occlusions) and in the point of view must be accounted. In this paper we propose a system based only on appearance information that, disregarding temporal and spatial information, can be flexibly applied on both moving and static cameras...
The mean shift tracker is commonly used in realtime target tracking. However, the original mean shift tracker employs only color feature and uses the Bhattacharya coefficient as similarity measure, resulting in low tracking accuracy. This paper proposed a novel tracking algorithm, which integrated color and texture features and employed histogram intersection and Powell's method to track. Firstly,...
This paper presents a face-tracking algorithm based on particle filter framework. Firstly, a target state is obtained using conventional color histograms. Secondly, a tracking method is proposed on the basis of seven moment invariants, and another state vector is also computed using this approach. Furthermore, the weights of the two state vectors are computed according to Euclidean distance between...
This paper proposes an approach for object tracking using particle filter based on an improved color correlogram. The improved color correlogram for representation object contains not only color information but also spatial information, which makes feature more distinctive. Instead of using the whole correlogram matrix as feature, we construct feature vector based on elements of the upper triangular...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.