Multiple-cue-based visual object contour tracking with incremental learning

This paper proposes a visual object contour tracking algorithm using a multi-cue fusion particle filter. A novel contour evolution energy is proposed which integrates an incrementally learnt model of object appearance with a parametric snake model. This energy function is combined with a mixed cascade particle filter tracking algorithm which fuses multiple observation models for object contour tracking. Bending energy due to contour evolution is modelled using a thin plate spline (TPS). Multiple order graph matching is performed between contours in consecutive frames. Both of the above are taken as observation models for contour deformation; these models are fused efficiently using a mixed cascade sampling process. The dynamic model used in our tracking method is further improved by the use of optical flow. Experiments on real videos show that our approach provides high performance object contour tracking

A Study of Exploiting Objectness for Robust Online Object Tracking

Tracking is a fundamental problem in many computer vision applications. Despite the progress over the last decade, there still exist many challenges especially when the problem is posed in real world scenarios (e.g., cluttered background, occluded objects). Among them drifting has been widely observed to be a problem common to the class of online tracking algorithms - i.e., when challenges such as occlusion or nonlinear deformation of the object occurs, the tracker might lose the target completely in subsequent frames in an image sequence. In this work, we propose to exploit the objectness to partially alleviate the drifting problem with the class of online object tracking and verify the effectiveness of this idea by extensive experimental results. More specifically, a recently developed objectness measure was incorporated into Incremental Learning for Visual Tracking (IVT) algorithm in a principled way. We have come up with a strategy of reinitializing the training samples in the proposed approach to improve the robustness of online tracking. Experimental results show that using objectness measure does help to alleviate its drift to background for certain challenging sequences

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

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Hypothesis-based image segmentation for object learning and recognition

Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition