TY - GEN
T1 - Design and FPGA Implementation of an Adaptive video Subsampling Algorithm for Energy-Efficient Single Object Tracking
AU - Iqbal, Odrika
AU - Siddiqui, Saquib
AU - Martin, Joshua
AU - Katoch, Sameeksha
AU - Spanias, Andreas
AU - Bliss, Daniel
AU - Jayasuriya, Suren
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Image sensors with programmable region-of-interest (ROI) readout are a new sensing technology important for energyefficient embedded computer vision. In particular, ROIs can subsample the number of pixels being readout while performing single object tracking in a video. In this paper, we develop adaptive sampling algorithms which perform joint object tracking and predictive video subsampling. We utilize an object detection consisting of either mean shift tracking or a neural network, coupled with a Kalman filter for prediction. We show that our algorithms achieve mean average precision of 0.70 or higher on a dataset of 20 videos in software. Further, we implement hardware acceleration of mean shift tracking with Kalman filter adaptive subsampling on an FPGA. Hardware results show a 23 × improvement in clock cycles and latency as compared to baseline methods and achieves 38FPS real-time performance. This research points to a new domain of hardware-software co-design for adaptive video subsampling in embedded computer vision.
AB - Image sensors with programmable region-of-interest (ROI) readout are a new sensing technology important for energyefficient embedded computer vision. In particular, ROIs can subsample the number of pixels being readout while performing single object tracking in a video. In this paper, we develop adaptive sampling algorithms which perform joint object tracking and predictive video subsampling. We utilize an object detection consisting of either mean shift tracking or a neural network, coupled with a Kalman filter for prediction. We show that our algorithms achieve mean average precision of 0.70 or higher on a dataset of 20 videos in software. Further, we implement hardware acceleration of mean shift tracking with Kalman filter adaptive subsampling on an FPGA. Hardware results show a 23 × improvement in clock cycles and latency as compared to baseline methods and achieves 38FPS real-time performance. This research points to a new domain of hardware-software co-design for adaptive video subsampling in embedded computer vision.
KW - FPGA acceleration
KW - adaptive subsampling
KW - embedded computer vision
KW - single object tracking
UR - http://www.scopus.com/inward/record.url?scp=85098651159&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098651159&partnerID=8YFLogxK
U2 - 10.1109/ICIP40778.2020.9191146
DO - 10.1109/ICIP40778.2020.9191146
M3 - Conference contribution
AN - SCOPUS:85098651159
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3065
EP - 3069
BT - 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PB - IEEE Computer Society
T2 - 2020 IEEE International Conference on Image Processing, ICIP 2020
Y2 - 25 September 2020 through 28 September 2020
ER -