Appearance Model For Object Tracking Research Articles

Adaptive fuzzy weighted color histogram and HOG appearance model for object tracking with a dynamic trained neural network prediction

Object tracking in video sequences represents an important task in computer vision applications like human mobility and social distance in pandemic scenarios, intelligent surveillance systems, etc. This paper presents a new method, fuzzy weighted color histogram neural network (FWCHNN) to estimate the position of an object and to update the appearance object model during tracking. The method involves two stages. Prediction is achieved through an online dynamic trained neural network, and correction is performed with a spiral sliding window. The method employs a new similitude appearance measure based on a codified model of the object considering its shape and color. The shape is represented with HOG features and the color features from information of a fuzzy weighted color histogram. FWCHNN shows the comparable results regarding state-of-the-art methods.

Multiple Reliable Structured Patches for Object Tracking

It is essential to build the effective appearance model for object tracking in computer vision. Most object trackers can be roughly divided into two categories according to the appearance model: the bounding box model and the patch model. The bounding box model cannot handle shape deformation and occlusion of the non-rigid moving object effectively. The patch model is prone to be disturbed by complex backgrounds. In this paper, we propose a robust multi-structured-patch appearance model to represent the target for object tracking. The proposed appearance model is aimed to exploit and identify reliable patches that can be tracked effectively through the whole tracking process. According to attention mechanism in biological vision system, a coarse-to-fine strategy is usually used to search the target. Therefore, the proposed appearance model is represented by robust patches in different sizes, in which the bigger patches search the rough region of the target and the smaller patches estimate the accurate location. Experimental results on OTB100 dataset show that the proposed method outperforms state-of-the-art trackers.

Visual Tracking With Weighted Adaptive Local Sparse Appearance Model via Spatio-Temporal Context Learning.

Sparse representation has been widely exploited to develop an effective appearance model for object tracking due to its well discriminative capability in distinguishing the target from its surrounding background. However, most of these methods only consider either the holistic representation or the local one for each patch with equal importance, and hence may fail when the target suffers from severe occlusion or large-scale pose variation. In this paper, we propose a simple yet effective approach that exploits rich feature information from reliable patches based on weighted local sparse representation that takes into account the importance of each patch. Specifically, we design a reconstruction-error based weight function with the reconstruction error of each patch via sparse coding to measure the patch reliability. Moreover, we explore spatio-temporal context information to enhance the robustness of the appearance model, in which the global temporal context is learned via incremental subspace and sparse representation learning with a novel dynamic template update strategy to update the dictionary, while the local spatial context considers the correlation between the target and its surrounding background via measuring the similarity among their sparse coefficients. Extensive experimental evaluations on two large tracking benchmarks demonstrate favorable performance of the proposed method over some state-of-the-art trackers.

Structural sparse coding seeds–active appearance model for object tracking

In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance model. The proposed object tracking scheme involves three stages: training, detection and tracking. In the training stage, an incremental SVM model that directly measures the candidates samples and target difference. The proposed mixture generate–discriminative method can well separate two highly correlated positive candidates images. In the detection stage, the trained weighted vector is used to separate the target object in positive candidates images with respect to the seeds images. In the tracking stage, we employ the particle filter to track the object through an appearance adaptive updating algorithm with seeds–active constrained sparse representation. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the current literature.

Structured partial least squares based appearance model for visual tracking

The appearance model is an important issue in the visual tracking. In this paper, we present a structured partial least squares (SPLS) based appearance model for object tracking which not only discriminates the target from its surrounding background but also able to tolerate its appearance variations. The target is represented as a low-dimensional feature vector, which is the structured combination of the subspaces learned by SPLS. In order to capture the variations in appearance during the tracking, we update the appearance model by taking the estimated class labels of the new targets obtained online into account. Furthermore, aiming to alleviate the drift problems, negative samples generated by disordering the structured appearance model are added in the training samples. Both qualitative and quantitative evaluations on challenging benchmark video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Efficient hybrid appearance model for object tracking with occlusion handling

During object tracking, the appearance of the object being tracked often suffers from complex variations due to large illumination, pose changes, and frequent occlusions. Modeling a robust appearance model for accommodating those variations becomes a crux for appearance-based tracking algorithms. We present a tracking algorithm based on the hybrid appearance models that include a fixed appearance model, a fast-change appearance model, and an eigenspace-based model. The hybrid appearance models reveal three factors of appearance changes: stable, transient, and gradual. A particle filter is invoked to perform state inference and simultaneously be responsible for intermodel switching. In addition, robust statistics is utilized to deal with occlusion events. Numerous experiments on many difficult sequences demonstrate that our algorithm can effectively track the object undergoing large pose, lighting, and viewpoint changes. More importantly, our algorithm shows powerful ability of addressing severe occlusions.