Illumination insensitive efficient second-order minimization for planar object tracking

Chen, Lin; Zhou, Fan; Shen, Yu; Tian, Xiang; Ling, Haibin; Chen, Yaowu

doi:10.1109/icra.2017.7989512

Cited by 23 publications

(19 citation statements)

References 27 publications

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“…Also, for ESM based algorithms [13], [14], [16], SCV [13] is a little better than the original ESM tracker [14] using the sum-of-squared-difference for appearance similarity measure. Though gradient orientations is robust to illumination change, the overall performance of GO-ESM [16] is worse than ESM [14]. At the same time, ESM, SCV and GO-ESM perform better than IC [15], implying that the efficient second-order minimization approach is better than the inverse compositional optimization approach for the planar object tracking task.…”

Section: B Evaluation Metricsmentioning

confidence: 98%

“…In this paper, we select four keypoint-based [9]- [12], four region-based [13]- [16] and three generic object tracking algorithms [17]- [19] as representative trackers in evaluation. The details of these algorithms are given in Sec.…”

Section: B Tracking Algorithmsmentioning

confidence: 99%

“…GO-ESM [16]: As gradient orientations (GO) is robust to illumination changes, it is used in GO-ESM along with denoising techniques to model the appearance of the target. GO-ESM also generalizes ESM to multidimensional features.…”

Section: A Selected Trackersmentioning

confidence: 99%

“…6(h)), in general, the keypoint-based trackers are more robust than the region-based trackers. The obvious performance difference can be attributed to the following two reasons: (1) though the image similarity measure SCV adopted by [13] or GO adopted by [16] are robust to illumination variations, their robustness is not comparable with the state-of-art keypoint detectors and descriptors or ferns; and (2) the keypoint-based algorithms use the trackingby-detection strategy and the detection in the current frame depends little on the object location in previous frames; by contrast, the region-based algorithms make use of the previous object state to reduce the optimization space for efficiency. Thus it is easier for keypoint-based trackers to recover from failure than region-based trackers.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

“…These algorithms include three types of trackers: four keypoint-based planar object tracking algorithms [9]- [12], four region-based (a.k.a. direct methods) planar object tracking algorithms [13]- [16], and three generic object tracking algorithms [17]- [19]. We use two performance metrics to analyze the evaluation results in details.…”

Section: Introductionmentioning

confidence: 99%

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Planar Object Tracking in the Wild: A Benchmark

Liang

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-theart 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.

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