Eye-to-Hand Robotic Visual Tracking Based on Template Matching on FPGAs

Chen, Zhong; Li, Shuai; Zhang, Ning; Hao, Yuya; Zhang, Xianmin

doi:10.1109/access.2019.2926807

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…They are used for optical flow determination, e.g., with the Lucas-Kanade and Horn-Schunck methods [4], or stereo correspondence with the Semi-Global Matching algorithm [5]. They also enable the implementation of advanced object tracking methods [6]. FPGAs can be applied in advanced driver assistance systems, for example for high-speed gaze detection [7], and unmanned aerial vehicles, for example the simultaneous localization and mapping [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

et al. 2022

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One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.

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Section: Introductionmentioning

confidence: 99%

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

et al. 2022

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“…V ISUAL tracking is one of the most fundamental research directions in computer vision, which has a capacity to infer the state of an arbitrary object in a sequence, only with its initial state in the first frame as reference. The technique is required by various visual issues, such as visual surveillance [1], robotics [2], human computer interaction [3] and augmented reality [4]. Despite great progress has been realized, most of trackers still struggle with several challenging factors, such as background clutters, occlusion, illumination variation, etc.…”

Section: Introductionmentioning

confidence: 99%

Siamese Visual Tracking With Residual Fusion Learning

2022

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Multi-stage feature fusion is pretty effective for deep Siamese trackers to promote tracking performance. Unfortunately, conventional fusion approaches, such as weighted average, are so simple that they are inappropriate to combine the features with diverse characteristics. In addition, the fusion module is generally optimized along with Siamese network module, which may result in the performance degradation of the whole tracker. In this paper, we propose a novel feature fusion network for Siamese tracker by exploiting the expression capacity of residual learning (SiamRFL). Specifically, the network employs the deep-layer features as direct input to semantically recognize the object from background, and refines the object state with local detail patterns by exploring the shallow-layer features through residual channel. The classification and the regression features can be fused respectively by deploying multiple fusion units. To avoid the degradation problem, we also present an ensemble training framework for our tracker, in which different loss functions are introduced to individually optimize the Siamese and the fusion modules. In the extensive experiments on several latest datasets including OTB100, VOT2019, UAV123, LaSOT and GOT10k, the proposed tracker achieves state-of-the-art performance, outperforming other approaches by an obvious margin.

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“…As an important part of computer vision, visual object tracking technology has been greatly developed with the rapid development of artificial intelligence technology and the continuous progress of hardware facilities in recent years. Visual object tracking has always been a challenging work in video surveillance [1], video understanding [2], autonomous driving [4], to robotics [3], navigation, positioning, and other fields. It often faces the influence of object occlusion, disappearance, morphological change, and other factors [5].…”

Section: Introductionmentioning

confidence: 99%

Triplet Network Template for Siamese Trackers

2021

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Siamese network based trackers describe object tracking as a similarity matching problem and these trackers achieve state-of-the-art performance on multiple benchmarks. However, due to the nonupdate of the appearance template and the change of the object appearance, the tracking drift problem often occurs, especially in the background clutter scene. Effective appearance template update methods can improve tracker performance, but most trackers use simple linear interpolation to update the template or do not update the initial template at all. Through experiments, we find that the channel response of the search region with the adjacent frame appearance template is often better than that with the initial frame appearance template. So we add the results of the previous frame prediction as a new template branch to the Siamese network to form a Triplet network. We applied the Triplet network to the SiamFC and SiamCAR, called TripFC and TripCAR. We tested on four challenging benchmarks (GOT-10K, OTB2013, OTB2015, UAV123). The experiments show that our method is powerful and effective, it can be easily embedded into the Siamese trackers. TripFC has a good effect on solving the problem of tracking drift. If necessary we can publish the code to facilitate research in this area.

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Eye-to-Hand Robotic Visual Tracking Based on Template Matching on FPGAs

Cited by 18 publications

References 25 publications

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

Siamese Visual Tracking With Residual Fusion Learning

Triplet Network Template for Siamese Trackers

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