Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.

A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by frame-by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera. Key words: Visua

Online Metric-Weighted Linear Representations for Robust Visual Tracking

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets

Dynamics of Attention in Depth: Evidence from Mutli-Element Tracking

The allocation of attention in depth is examined using a multi-element tracking paradigm. Observers are required to track a predefined subset of from two to eight elements in displays containing up to sixteen identical moving elements. We first show that depth cues, such as binocular disparity and occlusion through T-junctions, improve performance in a multi-element tracking task in the case where element boundaries are allowed to intersect in the depiction of motion in a single fronto-parallel plane. We also show that the allocation of attention across two perceptually distinguishable planar surfaces either fronto-parallel or receding at a slanting angle and defined by coplanar elements, is easier than allocation of attention within a single surface. The same result was not found when attention was required to be deployed across items of two color populations rather than of a single color. Our results suggest that, when surface information does not suffice to distinguish between targets and distractors that are embedded in these surfaces, division of attention across two surfaces aids in tracking moving targets.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Using spatio-temporal continuity constraints to enhance visual tracking of moving objects

We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-temporal continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-temporal continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video

Planar Object Tracking in the Wild: A Benchmark

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201