A fragment based scale adaptive tracker with partial occlusion handling

In this paper we propose a computationally efficient scale adaptive tracking method using a hybrid color histogram matching scheme. Firstly, we report an important property of the Chi-squared measure- It outperforms Bhattacharyya measure in the task of histogram matching from a few significantly similar multimodal histograms. Also, Bhattacharyya measure performs better while selecting matches from a varied dataset. We employ these results to develop a hybrid histogram matching procedure using the two measures. This method is used for a patch matching algorithm in real time tracking. We first calculate a color histogram of the target which is then compared with histograms of patches in the neighborhood in subsequent frames using this hybrid procedure to obtain the best match. We devise a systematic scale adaptive tracking method which is robust to rapid changes in the target size. It is also robust to partial occlusion of the target. Extensive experimental proof based on real life and test datasets is presented which demonstrates the excellent tracking accuracy achieved by our algorithm at real time.

This paper proposes a novel patch-based object tracking algorithm combining Harris-SIFT and color histogram features to handle the partial occlusion problem. The object is represented as a number of non-overlapping patches. Harris-SIFT feature is defined as Harris corner and its SIFT feature vector, which can represent steady parts of the object when it is partially transformed or occluded. The Harris-SIFT corner matching results are then used for filtering out invalid patches of the object. Patch-based color histogram takes spatial information into account and is guided by valid patch selection, thus provides a richer description of the object than the traditional color histogram. The average valid patch color histogram similarity is used in particle filter to locate the object. The experimental results show that the proposed algorithm is more accurate, robust and efficient than state-of-the-art object tracking algorithms in occlusion scenario.

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach.

Target tracking is a hot topic in the field of computer vision. It uses the context information of video or image sequence to model the appearance and motion information of the target, so as to predict the motion state of the target and calibrate the position of the target. From the perspective of building models, target tracking algorithms can be divided into generative models and discriminant models; From the number of tracking targets, it can be divided into single target tracking and multi-target tracking. Target tracking integrates the theories and algorithms of image processing, machine learning, optimization and other fields. It is the premise and foundation to complete higher-level image understanding.The basic task of target tracking is to give the initial position of the target in a video sequence and continuously track and locate the target in each subsequent frame. In this process, a priori conditions such as the color, shape and size of the target will not be provided, that is, the tracking algorithm can track only by learning the target in the first frame. The traditional solution is to calculate the project similarity offline and save it in the system for algorithm call, but it can not make full use of the latest scoring data to reflect the change of user interest. To solve the above problems, a target tracking item similarity incremental update mechanism suitable for online applications is proposed, so that the recommendation system can update the similarity data between the corresponding item and other items in real time after the current user submits the item score, so that the recommendation system can recommend based on the latest item similarity data, To adapt to changes in user interests.

Research on object tracking has been an active field because of its fundamental roles in surveillance and monitoring. In this paper, a new adaptive algorithm for fast target tracking based on hierarchical block matching is proposed. The improvement of the algorithm lies in three aspects. First, the hierarchical pyramid method is applied to enhance the search efficiency of the local features. In addition, at different pyramid level, an adaptive searching strategy is adopted according to the number of candidate features. Second, since image blocks make full use of local characters of features, the structure similarity method, associated with the relation coefficient in statistics, can be exploited to design a new matching criteria, which provides more precise matching results, especially in the complex scenes such as the change of illumination and partial occlusion. Lastly, traditional algorithms often track the object through adjacent frames, whereas our algorithm will search two frames with a variable interval on the basis of matching results. The intervals are obtained by using the bisection method and thus the searching time can be greatly saved. Simulation results show that the proposed block matching method has better performances in the searching time and accuracy in contrast to the classic Mean-Shift target tracking algorithm and Particle Filter target tracking algorithm under the complex circumstances such as illumination variation and partial occlusion.

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

A robust perception based method for iris tracking

Cascade of descriptors to detect and track objects across any network of cameras

Robust visual tracking base on adaptively multi-feature fusion and particle filter

In traditional Mean Shift algorithm, color histogram is usually used as the features vectors and the dissimilarity between the referenced targets and the target candidates is expressed by the metric derived from the Bhattacharyya coefficients. The traditional Mean Shift procedure is used to find the real position of the target by looking for the regional minimum of the distance function iteratively. While the target's color is similar to the background, the algorithm will miss the target. This paper presents a new mean shift algorithm based on spatial edge orientation histograms, using space distribution and texture information as matching information. Meanwhile a Kalman filter will be used to predict the target's position. Experimental results demonstrate that the proposed algorithm can deal with intricate conditions, such as significant clutter, partial occlusions, and it can track objects efficiently and robustly.

Human detection in a video surveillance system has vast application areas including suspicious event detection and human activity recognition. In the current environment of our society suspicious event detection is a burning issue. For that reason, this paper proposes a framework for detecting humans in different appearances and poses by generating a human feature vector. Initially, every pixel of a frame is represented as an incorporation of several Gaussians and use a probabilistic method to refurbish the representation. These Gaussian representations are then estimated to classify the background pixels from foreground pixels. Shadow regions are eliminated from foreground by utilizing a Hue-Intensity disparity value between background and current frame. Then morphological operation is used to remove discontinuities in the foreground extracted from the shadow elimination process. Partial occlusion handling is utilized by color correlogram to label objects within a group. After that, the framework generates ROIs by determining which of the foregrounds represent human by considering conditions related to human body. Finally, Histogram of Oriented Gradients (HOG) feature is extracted from ROI for classification. Various videos containing moving humans are utilized to evaluate the proposed framework and presented outcomes demonstrate the adequacy.

Most of conventional object matching methods are based on comparing local features, which are too computational demanding. Recently, Dominant Orientation Templates (DOT) were proposed to solve the efficiency issue. Although DOT obtains promising results, it still suffers the problem of wasting too many bits in representation and fragility when partial occlusion occurs. As the number of templates increase, the performance will decrease. Therefore, we propose a compact DOT representation with a fast partial occlusion handling approach. Instead of using seven orientations in the original implementation, we employ single orientation of the highest gradients for the proposed compact DOT representation (C-DOT). Consequently, the size of feature vectors is reduced from 8 bits to 3 bits. To efficiently tackle the partial occlusion, we introduce the C-DOT similarity map to store the matching scores of individual grids in each sliding window, which is used to further infer the occlusion map. The experimental results demonstrate that the proposed method outperforms DOT.

We present LensGesture, a pure software approach for augmenting mobile interactions with back-of-device finger gestures. LensGesture detects full and partial occlusion as well as the dynamic swiping of fingers on the camera lens by analyzing image sequences captured by the built-in camera in real time. We report the feasibility and implementation of LensGesture as well as newly supported interactions. Through offline benchmarking and a 16-subject user study, we found that 1) LensGesture is easy to learn, intuitive to use, and can serve as an effective supplemental input channel for today's smartphones; 2) LensGesture can be detected reliably in real time; 3) LensGesture based target acquisition conforms to Fitts' Law and the information transmission rate is 0.53 bits/sec; and 4) LensGesture applications can improve the usability and the performance of existing mobile interfaces.

The kernel bandwidth of the traditional Mean Shift tracking algorithm cannot be changed in real time. It can't achieve accurate tracking when the target size is changing. This paper proposes an algorithm to automatically select the bandwidth of the kernel function, which can achieve accurate tracking when the size of the rigid target is changing. Firstly, it matches the target center of two consecutive frames by using the backward tracking. Then in order to effectively eliminate the false matching and ensure the accuracy of the regression, it extracts and disposes the feature points with regressive calculation on the base of matching. The results of tracking experiment show that the proposed algorithm can achieve accurate tracking in real time when the target size is changing.

Recently, trilateral filter based method is more accurate than other local stereo matching methods on Middlebury benchmark. The boundary strength term is the key part of this method, which is computed from a color edge map. In this paper, we prove that boundary strength term, computed from a depth edge map, produces more accurate results. Then, we propose a depth edge detection technique to obtain an accurate depth edge map and present a depth edge based trilateral filter method. The experimental evaluation on the Middlebury benchmark shows that the proposed method outperforms the original trilateral filter method and also other local methods.