Multi-object tracking VLSI architecture using image-scan based region growing and feature matching

Yamaoka, Kousuke; Morimoto, Takashi; Adachi, H.; Awane, Kazutoshi; Koide, Tetsushi; Mattausch, Hans Jürgen

doi:10.1109/iscas.2006.1693898

Cited by 11 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tracking in Hardware and Tiny ML. Researchers have also been striving to optimize hardware for tracking tasks [35], [57]- [59]. Other works have shown how FPGA acceleration can be leveraged for latency optimization of computationally expensive tracking algorithms [60]- [66].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Subsampling for ROI-Based Visual Tracking: Algorithms and FPGA Implementation

et al. 2022

View full text Add to dashboard Cite

There is tremendous scope for improving the energy efficiency of embedded vision systems by incorporating programmable region-of-interest (ROI) readout in the image sensor design. In this work, we study how ROI programmability can be leveraged for vision applications by anticipating where the ROI will be located in future frames and switching pixels off outside of this region. We refer to this process of ROI prediction and corresponding sensor configuration as adaptive subsampling. Our adaptive subsampling algorithms comprise an object detector and an ROI predictor (Kalman filter) which operate in conjunction to optimize the energy efficiency of the vision pipeline with the end task being object tracking. To further facilitate the implementation of our adaptive algorithms in real systems, we select a candidate algorithm and map it onto an FPGA. Leveraging Xilinx Vitis AI tools, we designed and accelerated a YOLO object detector-based adaptive subsampling algorithm. In order to further improve the algorithm post-deployment, we evaluated several competing baselines on the OTB100 and LaSOT datasets. We found that coupling the ECO tracker with the Kalman filter has a competitive AUC score of 0.4568 and 0.3471 on the OTB100 and LaSOT datasets respectively. Further, the power efficiency of this algorithm is on par with, and in a couple of instances superior to, the other baselines. The ECO-based algorithm incurs a power consumption of approximately 4 W averaged across both datasets while the YOLO-based approach requires power consumption of approximately 6 W (as per our power consumption model). In terms of accuracy-latency tradeoff, the ECO-based algorithm provides near-real-time performance (19.23 FPS) while managing to attain competitive tracking precision.INDEX TERMS Adaptive subsampling, embedded computer vision, FPGA acceleration, hardware/software co-design, single object tracking, vision applications

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive Subsampling for ROI-Based Visual Tracking: Algorithms and FPGA Implementation

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This is why energy-efficient object tracking has been studied over the years [29], [30], [31], [32], [33]. Researchers have also been striving to optimize hardware for tracking tasks [34], [35], [36], [37].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation

Iqbal¹,

Muro²,

Katoch³

et al. 2021

Preprint

View full text Add to dashboard Cite

There is tremendous scope for improving the energy efficiency of embedded vision systems by incorporating programmable region-of-interest (ROI) readout in the image sensor design. In this work, we study how ROI programmability can be leveraged for tracking applications by anticipating where the ROI will be located in future frames and switching pixels off outside of this region. We refer to this process of ROI prediction and corresponding sensor configuration as adaptive subsampling. Our adaptive subsampling algorithms comprise an object detector and an ROI predictor (Kalman filter) which operate in conjunction to optimize the energy efficiency of the vision pipeline with the end task being object tracking. To further facilitate the implementation of our adaptive algorithms in real life, we select a candidate algorithm and map it onto an FPGA. Leveraging Xilinx Vitis AI tools, we designed and accelerated a YOLO object detector-based adaptive subsampling algorithm. In order to further improve the algorithm post-deployment, we evaluated several competing baselines on the OTB100 and LaSOT datasets. We found that coupling the ECO tracker with the Kalman filter has a competitive AUC score of 0.4568 and 0.3471 on the OTB100 and LaSOT datasets respectively. Further, the power efficiency of this algorithm is on par with, and in a couple of instances superior to, the other baselines. The ECO-based algorithm incurs a power consumption of approximately 4 W averaged across both datasets while the YOLO-based approach requires power consumption of approximately 6 W (as per our power consumption model). In terms of accuracy-latency tradeoff, the ECO-based algorithm provides near-real-time performance (19.23 FPS) while managing to attain competitive tracking precision.

show abstract

“…Yamaoka et al [58] proposed a segmentation technique based on region‐growing image‐scan that exploits the large access bandwidth feature in the embedded memories. This type of memories allows reading the results of segmenting one row of the image in one clock cycle.…”

Section: Review Of the Hardware Implementations Of The Tracking Sysmentioning

confidence: 99%

Survey on hardware implementations of visual object trackers

El-Shafie

Habib

2019

IET Image Processing

View full text Add to dashboard Cite

Multi-object tracking VLSI architecture using image-scan based region growing and feature matching

Cited by 11 publications

References 2 publications

Adaptive Subsampling for ROI-Based Visual Tracking: Algorithms and FPGA Implementation

Adaptive Subsampling for ROI-Based Visual Tracking: Algorithms and FPGA Implementation

Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation

Survey on hardware implementations of visual object trackers

Contact Info

Product

Resources

About