Khochare, A and Krishnan, A and Simmhan, Y (2021) A Scalable Platform for Distributed Object Tracking across a Many-Camera Network. In: IEEE Transactions on Parallel and Distributed Systems, 32 (6). pp. 1479-1493.
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Abstract
Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking platforms have two key limitations: 1) They are monolithic, proprietary and lack the ability to rapidly incorporate sophisticated tracking models, and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog, and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance, and the active camera-set size. We illustrate the concise expressiveness of the programming model for four tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance, and quality trade-offs enabled by our dynamic tracking, batching, and dropping strategies.
| Item Type: | Journal Article |
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| Publication: | IEEE Transactions on Parallel and Distributed Systems |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Cameras; Computer systems programming; Data flow analysis; Deep neural networks; Economic and social effects; Image processing, Dataflow programming; Distributed objects; Distributed resources; Distributed tracking; Large-scale deployment; Programming models; Real time performance; Tracking application, Object tracking |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 16 May 2023 09:37 |
| Last Modified: | 16 May 2023 09:37 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81677 |
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