Optimal Metric Evaluation-Based Multicue Inverse Sparse Appearance Model for Object Tracking

Dual-scale structural local sparse appearance model for robust object tracking

We present a method that integrates a part-based sparse appearance model in a Bayesian inference framework for tracking targets in video sequences. We formulate the sparse appearance model as a set of smoothed colour histograms corresponding to the object windows detected by the Deformable Part Model (DPM) detector. The data association of each body part between frames is solved based on the position constraint, appearance coherence, and motion consistency. To deal with missing and noisy observations, the part detection window in the following frame is also predicted using an interacting multiple model (IMM) tracker. We have tested our tracking method on all the video sequences that involve people in upright poses from the TB-50 and TB-100 benchmark videos datasets. Our experimental results show that our tracking method outperforms six state-of-the-art tracking techniques.

Effective object appearance model is one of the key issues for the success of visual tracking. Since the appearance of a target and the environment changes dynamically, the majority of existed visual tracking algorithms tend to drift away from targets. To address this issue, we propose a robust tracking algorithm by integrating the generative and discriminative model. The object appearance model is made up of generative target model and a discriminative classifier. For the generative target model, we adopt the weighted structural local sparse appearance model combining patch based gray value and Histogram of Oriented Gradients feature as the patch dictionary. By sampling positives and negatives, alignment-pooling features are obtained based on the patch dictionary through local sparse coding, then we use support vector machine to train the discriminative classifier. The proposed method is embedded into a Bayesian inference framework for visual tracking. A combined matching method is adopted to improve the proposal distribution of the particle filter. Moreover, in order to adapt the situation change, the patch dictionary and discriminative classifier are updated by incremental learning every five frames. Experimental results on some publicly available benchmarks of video sequences demonstrate the accuracy and effectiveness of our tracker.

Particle filters have been proven very successful for non-linear and non-Gaussian estimation problems and extensively used in object tracking. However, high computational costs and particle decadency problem limit its practical application. In this paper, we present a robust particle tracking approach based on spatio-temporal context learning and multi-task joint local sparse representation. The proposed tracker samples particles according to the confidence map constructed by the spatio-temporal context information of the target. This sampling strategy can ameliorate problems of sample impoverishment and particle degeneracy, target state distribution to obtain robust tracking performance. In order to locate the target more accurately and be less sensitive to occlusion, the local sparse appearance model is adopted to capture the local and structural information of the target. Finally, the multi-task learning where the representations of particles are learned jointly is employed to further improve tracking performance and reduce overall computational complexity. Both qualitative and quantitative evaluations on challenging benchmark image sequences have demonstrated that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance models, which are key elements for good performance. Many previous methods either use simple features such as color histograms, or focus on the discriminability between a target and the background which does not resolve ambiguities between the different targets. We propose an algorithm for learning a discriminative appearance model for different targets. Training samples are collected online from tracklets within a time sliding window based on some spatial-temporal constraints; this allows the models to adapt to target instances. Learning uses an Ad-aBoost algorithm that combines effective image descriptors and their corresponding similarity measurements. We term the learned models as OLDAMs. Our evaluations indicate that OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches.

In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Track lets, the short-term tracking results, are generated by linking detections in consecutive frames based on conservative constraints. Our goal is to "re-stitching" the adjacent track lets that contain the same target so that robust long-term tracking results can be achieved. As the appearance of the same target may change greatly due to heavy occlusion, pose variations and changing lighting conditions, a discriminative appearance model is crucial for association-based tracking. Unlike most previous methods which simply use the similarity of color histograms or other low level features to construct the appearance model, we propose to use the fusion of soft biometrics generated from sub-track lets to learn a discriminative appearance model in an online manner. Compared to low level features, soft biometrics are robust against appearance variation. The experimental results demonstrate that our method is robust and greatly improves the tracking performance over the state-of-the-art method.

An appearance model is critical for most modern trackers. While numerous novel appearance models have been proposed with demonstrated success, challenges such as occlusion and drifting are still not well addressed. In this paper, we propose a novel contextual bag-of-words (CBOW) discriminative appearance model that appropriately handles drifting and occlusion. Specifically, a contextual region containing both the target and its surroundings is explored to construct a compact representation with two bags-of-words. Each word carries discriminative appearance information that is learned by Bayesian inference. An adaptive updating approach, where the background BOWs of the CBOW model acts as a "sentinel" to prevent the integration of the background appearance with the object model, is introduced to alleviate the drifting problem. Based on CBOW, visual tracking is posed within a Bayesian framework. Moreover, an explicit detection method is employed to handle severe occlusions, which further reduces drifting. Two trackers based on the same CBOW model are implemented using either handcrafted color/texture or deep convolutional features. Our trackers are evaluated based on the popular OTB50 and VOT2015 benchmarks and perform competitively against the current state of the art. In addition, they outperform two recent BOWs trackers by a large margin using the currently available figures of merit. To take into account a tracking breakdown, we propose a new figure of merit called the mean maximum-tracked-frame ratio (MTFR) that evaluates a tracker's temporal persistence without any interruption. Experiments with OTB50 demonstrate the superior robustness of our tracker compared with all other evaluated trackers on the basis of MTFR.

Tracking-by-detection methods have been widely studied and some promising results have been obtained. These methods use discriminative appearance models to train and update online classifiers. They also use a sliding window to detect samples which will then be classified. Then, the location of the sample with the maximum classifier response will be selected as the new location. Compressive tracking was recently proposed with an appearance model based on features extracted in the compressed domain. However, CT uses a fixed-size tracking box to detect samples, and this is unsuitable for practice applications. CT detects samples around the selected region of the previous frame within a fixed radius. Here, the classifier may become inaccurate if the selected region drifts. The fixed radius is also not suitable for tracking targets that experience abrupt acceleration changes. Furthermore, CT updates the classifier parameters with constant learning rate. If the target is fully occluded for an extended period, the classifier will instead learn the features of the cover object and the target will ultimately be lost. In this paper, we present a multi-scale compressive tracker. This tracker integrates an improved appearance model based on normalized rectangle features extracted in the adaptive compressive domain into the bootstrap filter. This type of feature extraction is efficient, and the computation complexity does not increase as the tracking regions become larger. The classifier response is utilized to generate particle importance weight and a re-sample procedure preserves samples according to weight. A 2-order transition model considers the target velocity to estimate the current position and scale status. In this way, the sampling is not limited to a fixed range. Here, feedback strategies are adopted to adjust learning rate for occlusion. Experimental results on various benchmark challenging sequences have demonstrated the superior performance of our tracker when compared with several state-of-the-art tracking algorithms.

Enhancing discriminative appearance model for visual tracking

Co-trained generative and discriminative trackers with cascade particle filter

Automatic and precise segmentation of hand bones is important for many medical imaging applications. Although several previous studies address bone segmentation, automatically segmenting articulated hand bones remains a challenging task. The highly articulated nature of hand bones limits the effectiveness of atlas-based segmentation methods. The use of low-level information derived from the image-of-interest alone is insufficient for detecting bones and distinguishing boundaries of different bones that are in close proximity to each other. In this study, we propose a method that combines an articulated statistical shape model and a local exemplar-based appearance model for automatically segmenting hand bones in CT. Our approach is to perform a hierarchical articulated shape deformation that is driven by a set of local exemplar-based appearance models. Specifically, for each point in the shape model, the local appearance model is described by a set of profiles of low-level image features along the normal of the shape. During segmentation, each point in the shape model is deformed to a new point whose image features are closest to the appearance model. The shape model is also constrained by an articulation model described by a set of pre-determined landmarks on the finger joints. In this way, the deformation is robust to sporadic false bony edges and is able to fit fingers with large articulations. We validated our method on 23 CT scans and we have a segmentation success rate of ~89.70 %. This result indicates that our method is viable for automatic segmentation of articulated hand bones in conventional CT.

In this paper, the consistent fish tracking strategies have been present for the underwater surveillance system with multiple static cameras in the overlapping field of views (FOV). The discriminative appearance model has been first introduced to distinguish the swimming fish and the background by the superpixel. The centroid coordinate homographic mapping and the Speeded Up Robust Features (SURF) technique have been taken to capture and match the same fish from the perspectives among multiple cameras by a set of strategies. Simulation experiments have shown that the proposed method is less dependent on the exact motion detection of the fish and could perform robustly and stably at a high speed in case of the occlusion and appearance variation under the sea.

We develop an algorithm for finding and kinematically tracking multiple people in long sequences. Our basic assumption is that people tend to take on certain canonical poses, even when performing unusual activities like throwing a baseball or figure skating. We build a person detector that quite accurately detects and localizes limbs of people in lateral walking poses. We use the estimated limbs from a detection to build a discriminative appearance model; we assume the features that discriminate a figure in one frame will discriminate the figure in other frames. We then use the models as limb detectors in a pictorial structure framework, detecting figures in unrestricted poses in both previous and successive frames. We have run our tracker on hundreds of thousands of frames, and present and apply a methodology for evaluating tracking on such a large scale. We test our tracker on real sequences including a feature-length film, an hour of footage from a public park, and various sports sequences. We find that we can quite accurately automatically find and track multiple people interacting with each other while performing fast and unusual motions.

Abnormal event detection and automated alarm are the important tasks in visual surveillance applications. In this paper, a novel automated alarm method based on intelligent visual analysis is proposed for alarm of abandoned objects and virtual cordon protection. Firstly the monitoring regions and cordons position are set artificially in the surveillance background scenes. The forground motion regions are segmented based on background subtraction model, and then are clustered by connected component analysis. After motion region segmentation and cluster, object tracking based on discriminative appearance model for monocular multi-target tracking is utilized. According to the motion segmentation and tracking results, alarm is triggered in comparison with the monitoring regions and cordons position. Experimental results show that the proposed automated alarm algorithms are sufficient to detect the abnormal events for alarm of abandoned objects and virtual cordon protection.

Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios.