Visual tracking via guided filter

Du, Dandan; Lu, Huchuan; Zhang, Lihe; Li, Fu

doi:10.1109/icip.2015.7351107

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…We take this work as a guide to understand the variance of error values in datasets of depth maps. In [22], a robust GF-based visual tracking system is proposed. The work shows that the ability to keep the edges of the image smooth is the advantage of the system.…”

Section: Related Workmentioning

confidence: 99%

Weighted Least Square Filter for Improving the Quality of Depth Map on FPGA

Mao

Wei

Amano

et al. 2022

IJNC

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This paper proposes a post-filtering system for improving the quality of depth maps for 3D projection on FPGA. We propose to implement the Weighted Least Square (WLS) filter on Field-programmable Gate Array (FPGA), which can predict the disparities, which cannot be measured, by using the values of the neighboring pixels. In our design, we optimized the architecture of WLS filter at the algorithm level. For hardware acceleration, we use the High-Level Synthesis (HLS) description to accelerate the algorithm. To break through the bottleneck brought by the limited memory resources on FPGA, we used UltraScale Architecture Memory Resources (URAM) on board and reduced memory consumption of Block RAMs (BRAM) from 140% to 80%. Through our approach, we can improve the quality of the depth map on the FPGA named M-KUBOS. And, the WLS filter can smooth the depth map with 130 MHz operational frequency on M-KUBOS at the speed of 10 frames per second (FPS), which achieves a 76.43% performance acceleration compared to the software execution on the ARM Cortex A9 at 1.2 GHz clock.

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Section: Related Workmentioning

confidence: 99%

Weighted Least Square Filter for Improving the Quality of Depth Map on FPGA

Mao

Wei

Amano

et al. 2022

IJNC

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“…Note that we focus on the update scheme of positive templates in this work. As for the negative templates, we simply remove the templates in the previous and add the new templates from the last frame [42]. Before introducing the scheme of template update, we propose an index called average cosine similarity to judge whether the target appearance changes.…”

Section: ) Framework Of Particle Filtermentioning

confidence: 99%

Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation

Wan

Qian

et al. 2019

IEEE Internet Things J.

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Target tracking based on unmanned aerial vehicle (UAV) video is a significant technique in intelligent urban surveillance systems for smart city applications, such as smart transportation, road traffic monitoring, inspection of stolen vehicle, etc. In this paper, a vision-based target tracking algorithm aiming at locating UAV-captured targets, like pedestrian and vehicle, is proposed using sparse representation theory. First of all, each target candidate is sparsely represented in the subspace spanned by a joint dictionary. Then, the sparse representation coefficient is further constrained by an L2 regularization based on the temporal consistency. To cope with the partial occlusion appearing in UAV videos, a Markov Random Field (MRF)-based binary support vector with contiguous occlusion constraint is introduced to our sparse representation model. For long-term tracking, the particle filter framework along with a dynamic template update scheme is designed. Both qualitative and quantitative experiments implemented on visible (Vis) and infrared (IR) UAV videos prove that the presented tracker can achieve better performances in terms of precision rate and success rate when compared with other state-of-the-art trackers.

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Context multi-task visual object tracking via guided filter

Wang

Luo

et al. 2018

Signal Processing: Image Communication

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Visual tracking via guided filter

Cited by 5 publications

References 19 publications

Weighted Least Square Filter for Improving the Quality of Depth Map on FPGA

Weighted Least Square Filter for Improving the Quality of Depth Map on FPGA

Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation

Context multi-task visual object tracking via guided filter

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