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Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects

Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. Hamed Pirsiavash, Deva Ramanan , Charless Fowlkes Department of Computer Science, UC Irvine. Estimate number of tracks and their extent Do not initialize manually Estimate birth and death of each track.

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Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects

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  1. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects Hamed Pirsiavash, Deva Ramanan, CharlessFowlkes Department of Computer Science, UC Irvine

  2. Estimate number of tracks and their extent • Do not initialize manually • Estimate birth and death of each track

  3. Our approach: Graph theoretic problem • Globally Optimal • for a common class of objective functions • Locally Greedy • and hence straightforward to implement • Scale linearly in the number of objects and video length

  4. Object state • Object track • K-object tracker • Discretize state space S (e.g., scanning window locations) • Assume no tracks overlap (for now) • Must infer K, track births & deaths, and solve data association

  5. Trellis graph • Local cost of window • Pairwise cost of transition • Dynamic programming finds a single track • (Viterbi algorithm)

  6. Trellis graph Add edges to model occlusion • Local cost of window • Pairwise cost of transition • Dynamic programming finds a single track • (Viterbi algorithm)

  7. What about variable length tracks? Birth cost Death cost How to find more than one track?

  8. Equivalent graph problem: Min-cost-flow A generalization of min-cut/max-flow problem Introduced in “Zhang, Li, Nevatia, CVPR’08” Input flow of d A transition A detection window Output flow of d

  9. Our contribution Find 4-track solution given a 3-track solution DP

  10. Our contribution Find 4-track solution given a 3-track solution DP Sub-optimum Optimum

  11. Our contribution Find 4-track solution given a 3-track solution DP Sub-optimum SSP Optimum Shortest path: New track can “suck” flow from existing tracks

  12. Solutions • Globally optimum • Previous work • Zhang et al CVPR’08: Introduced the model with a naïve solver • Our algorithm • Exploits the special structure of graph (DAG, unit-capacity) • Is greedy usingsuccessive shortest path • Approximate • Dynamic programming • Is greedy

  13. Why is greedy nice? Non-max-suppression in the loop: • At each iteration, suppress all windows overlapping with the instanced track. One iteration

  14. Experiments Datasets: • Caltech pedestrian dataset • Camera on a moving car • ~120,000 frames • ETHMS dataset • Moving camera on a cross walk • ~1000 frames

  15. Detection vs false positive per frame (FFPI) for ETHMS dataset

  16. Detection vs false positive per frame (FFPI) for ETHMS dataset

  17. Detection vs false positive per frame (FFPI) for ETHMS dataset

  18. Novel, scalable,greedy algorithm with state-of-the-art results

  19. Thanks

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