@InProceedings{8902829,
  author = {S. Banerjee and J. G. Serra and H. H. Chopp and O. Cossairt and A. K. Katsaggelos},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {An Adaptive Video Acquisition Scheme for Object Tracking},
  year = {2019},
  pages = {1-5},
  abstract = {In this paper, we propose an adaptive host-chip system for video acquisition constrained under a given bit rate to optimize object tracking performance. The chip is an imaging instrument with limited computational power consisting of a very high-resolution focal plane array (FPA) that transmits quadtree (QT)-segmented video frames to the host. The host has unlimited computational power for video analysis. We find the optimal QT decomposition to minimize a weighted rate distortion equation using the Viterbi algorithm. The weights are user-defined based on the class of objects to track. Faster R-CNN and a Kalman filter are used to detect and track the objects of interest respectively. We evaluate our architecture's performance based on the Multiple Object Tracking Accuracy (MOTA).},
  keywords = {convolutional neural nets;focal planes;image filtering;image resolution;image segmentation;Kalman filters;object detection;object tracking;optimisation;quadtrees;rate distortion theory;recurrent neural nets;video signal processing;adaptive video acquisition scheme;adaptive host-chip system;imaging instrument;unlimited computational power;video analysis;optimal QT decomposition;Multiple Object Tracking Accuracy;object tracking performance optimization;high-resolution focal plane array;quadtree-segmented video frames;weighted rate distortion equation minimization;faster R-CNN;Kalman filter;object detection;MOTA;architecture performance;Viterbi algorithm;Distortion;Object tracking;Viterbi algorithm;Bandwidth;Image reconstruction;Detectors;host-chip architecture;Viterbi algorithm;optimal bit allocation;rate distortion;object tracking},
  doi = {10.23919/EUSIPCO.2019.8902829},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570534035.pdf},
}

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K."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["S."],"propositions":[],"lastnames":["Banerjee"],"suffixes":[]},{"firstnames":["J.","G."],"propositions":[],"lastnames":["Serra"],"suffixes":[]},{"firstnames":["H.","H."],"propositions":[],"lastnames":["Chopp"],"suffixes":[]},{"firstnames":["O."],"propositions":[],"lastnames":["Cossairt"],"suffixes":[]},{"firstnames":["A.","K."],"propositions":[],"lastnames":["Katsaggelos"],"suffixes":[]}],"booktitle":"2019 27th European Signal Processing Conference (EUSIPCO)","title":"An Adaptive Video Acquisition Scheme for Object Tracking","year":"2019","pages":"1-5","abstract":"In this paper, we propose an adaptive host-chip system for video acquisition constrained under a given bit rate to optimize object tracking performance. The chip is an imaging instrument with limited computational power consisting of a very high-resolution focal plane array (FPA) that transmits quadtree (QT)-segmented video frames to the host. The host has unlimited computational power for video analysis. We find the optimal QT decomposition to minimize a weighted rate distortion equation using the Viterbi algorithm. The weights are user-defined based on the class of objects to track. Faster R-CNN and a Kalman filter are used to detect and track the objects of interest respectively. 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Banerjee and J. G. Serra and H. H. Chopp and O. Cossairt and A. K. Katsaggelos},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {An Adaptive Video Acquisition Scheme for Object Tracking},\n year = {2019},\n pages = {1-5},\n abstract = {In this paper, we propose an adaptive host-chip system for video acquisition constrained under a given bit rate to optimize object tracking performance. The chip is an imaging instrument with limited computational power consisting of a very high-resolution focal plane array (FPA) that transmits quadtree (QT)-segmented video frames to the host. The host has unlimited computational power for video analysis. We find the optimal QT decomposition to minimize a weighted rate distortion equation using the Viterbi algorithm. The weights are user-defined based on the class of objects to track. Faster R-CNN and a Kalman filter are used to detect and track the objects of interest respectively. 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