Joint Feature Correspondences and Appearance Similarity for Robust Visual Object Tracking

This paper addresses the issue of tracking a single visual object through crowded scenarios, where a target object may be intersected or partially occluded by other objects for a long duration, experience severe deformation and pose changes, and different motion speed in cluttered background. A robust visual object tracking scheme is proposed that exploits the dynamics of object shape and appearance similarity. The method uses a particle filter where a multi-mode anisotropic mean shift is embedded to improve the initial particles. Comparing with the conventional particle filter and mean shift-based tracking (Shan et al. 2004), our method offers the following novelties: We employ a fully tunable rectangular bounding box described by five parameters (2D central location, width, height, and orientation) and full functionaries in the joint tracking scheme; We derive the equations for the multi-mode version of the anisotropic mean shift where the rectangular bounding box is partitioned into concentric areas, allowing better tracking objects with multiple modes. The bounding box parameters are then computed by using eigen-decomposition of mean shift estimates and weighted averaging. This enables a more efficient redistributions of initial particles towards locations associated with large weights, hence an efficient particle filter tracking using a very small number of particles (N = 15 is used). Experiments have been conducted on video containing a range of complex scenarios, where tracking results are further evaluated by using two objective criteria and compared with two existing tracking methods. Our results have shown that the propose method is robust in terms of tracking drift, tightness and accuracy of tracked bounding boxes, especially in scenarios where the target object contains long-term partial occlusions, intersections, severe deformation, pose changes, or cluttered background with similar color distributions.

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“…In such a scenario, other strategy, e.g. using local object features [37,38], could be combined for improving the tracking. …”

Section: Discussionmentioning

confidence: 99%

A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity

Khan

Backhouse

2010

J Sign Process Syst

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“…Among these, the most famous and widely-used algorithms [32] are Kalman filter-based tracking [16], multiple hypothesis tracking (MHT) [47], optical flow-based tracking [56], particle filter tracking [27], point feature tracking, and mean shift tracking [12]. A detailed analysis and comparison of the existing approaches is beyond the aim of the paper, and an extensive literature of object tracking approaches may be found in [70] and [49].…”

Section: Object Detection Ad Tracking Under Extreme Conditionsmentioning

confidence: 99%

Understanding fish behavior during typhoon events in real-life underwater environments

Spampinato

Palazzo

Boom

et al. 2012

Multimed Tools Appl

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The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan's coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting This research was funded by European Commission FP7 grant 257024, for the Fish4Knowledge project (www.fish4knowledge.eu). Appl (2014) 70:199-236 aspects from the biologists' point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of "typhoon" events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.

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“…Intelligent video surveillance [1] is of great interests in industry applications due to the increasing demand to reduce the manpower of analysing the large-scale video data. Key technologies have been developed for intelligent surveillance, such as object tracking [2], [3], pedestrian detection [4] gait analysis [5], vehicle template recognition [6], privacy protection [7], face and iris recognition [8], video summarization [9], and crowd counting [10]. In this paper, we focus on video anomaly detection (also named as outlier detection), i.e.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Using Markov-Modulated Poisson Process for Video Surveillance

Rannels¹,

Falconieri²,

Phillips³

et al. 2017

IJRTER

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Joint Feature Correspondences and Appearance Similarity for Robust Visual Object Tracking

Cited by 25 publications

References 36 publications

A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity

A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity

Understanding fish behavior during typhoon events in real-life underwater environments

Anomaly Detection Using Markov-Modulated Poisson Process for Video Surveillance

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