Visual tracking using compensated motion model for mobile cameras

Lu, Jian-Yi; Wei, Yi-Chun; Tang, Angela Chih-Wei

doi:10.1109/icip.2011.6116558

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…As to color vision, physicists generally support the trichromatic theory (Young-Helmholtz theory) [28] while psychologists often agree with opponent-process theory [29]. Thus, based on the particle filter with compensated motion model [23], particle weights are optimized using both RGB and [I, R-G, Y-B] color histograms on the mixed images in [22]. To improve tracking accuracy, the [I, R-G, Y-B] color histogram is computed for the intersection of the region indicated by the dynamic layer, extracted by layer separation [7], and the rectangular region centered at one particle.…”

Section: The Proposed Tracking Scheme Using Visual Cue Integration Fomentioning

confidence: 99%

“…The compensated motion model that includes the control vector of camera motion can improve the tracking accuracy significantly [23]. In [23], the state vector x i t of the ith particle at time t is predicted by [27], ½G x;t ; G y;t T is camera motion, and ½W ðiÞ x;t ; W ðiÞ y;t T is the object motion on the 2-D image after motion compensation.…”

Section: Overview Of the Compensated Motion Model For Tracking On Mobmentioning

confidence: 99%

“…Since the focus of tracking is the target but neither the transmitted scene nor the reflected scene, layer separation [13] also extracts the dynamic layer before single object tracking in [22]. Based on the framework of particle filter with compensated motion model [23], the correction stage reweights particles using RGB and [I, R-G, Y-B] color histograms of the mixed images [20]. The [I, R-G, Y-B] color histogram is generated with the aid of a mask, indicating the dynamic regions on the mixed image.…”

Section: Introductionmentioning

confidence: 99%

“…The remainder of this paper is organized as follows. Section 2 reviews the compensated motion model for tracking with mobile cameras [23]. Section 3 proposes motion compensated layer separation for images with camera motion.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust tracking using visual cue integration for mobile mixed images

Chen

Tang

2015

Journal of Visual Communication and Image Representation

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Section: The Proposed Tracking Scheme Using Visual Cue Integration Fomentioning

confidence: 99%

Section: Overview Of the Compensated Motion Model For Tracking On Mobmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust tracking using visual cue integration for mobile mixed images

Chen

Tang

2015

Journal of Visual Communication and Image Representation

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“…Some work has also been done on the search strategy [22], [43]. In real applications, the motion of target is hard to define, especially when the video is captured by a moving camera, therefore just a few works pay attention to the motion model [20], [28].…”

mentioning

confidence: 99%

Robust Online Learned Spatio-Temporal Context Model for Visual Tracking

Wen

Cai

Lei

et al. 2014

IEEE Trans. on Image Process.

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Abstract-Visual tracking is an important but challenging problem in the computer vision field. In the real world, the appearances of the target and its surroundings change continuously over space and time, which provides effective information to track the target robustly. However, enough attention has not been paid to the spatio-temporal appearance information in previous works. In this paper, a robust spatio-temporal context model based tracker is presented to complete the tracking task in unconstrained environments. The tracker is constructed with temporal and spatial appearance context models. The temporal appearance context model captures the historical appearance of the target to prevent the tracker from drifting to the background in a long-term tracking. The spatial appearance context model integrates contributors to build a supporting field. The contributors are the patches with the same size of the target at the key-points automatically discovered around the target. The constructed supporting field provides much more information than the appearance of the target itself, and thus, ensures the robustness of the tracker in complex environments. Extensive experiments on various challenging databases validate the superiority of our tracker over other state-of-the-art trackers.

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