Transitive Re-identification Algorithm in Surveillance Networks
"Explore the Transitive Re-identification Algorithm for enhancing person tracking in multi-camera surveillance networks. This research delves into the motivation, experiments, and future implications of this cutting-edge technique."
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Introduction Motivation The Transitive Alg. Experiments Summary & Future Work Transitive Re-identification Yulia Brand? Tamar Avraham? Michael Lindenbaum? ?Electrical Engineering Department ?Computer Science Department Technion - I.I.T. Haifa, Israel This research was supported by the MAGNET program in the Israeli ministry of industry and commerce, by the Israeli ministry of science and by the E. and J. Bishop research fund. BMVC 2013
Introduction ReIDentification (ReID) Motivation The Transitive Alg. Experiments Summary & Future Work Camera A, time t Camera B, time t+ t upper image from: A. Bialkowski, S. Denman, P. Lucey, S. Sridharan, and C. B. Fookes. A database for person re-identification in multi- camera surveillance networks. (DICTA 2012) lower images: courtesy of Marco Cristani
Introduction ReIDentification Motivation The Transitive Alg. Experiments Summary & Future Work
Introduction ReIDentification Motivation The Transitive Alg. Experiments Summary & Future Work 100 ICT learning based 90 80 SDALF - direct Recognition % 70 60 50 40 30 ICT trained on 3-7 data SDALF for cameras 3-7 20 10 1 2 3 4 5 6 7 8 9 10 11 12 rank score
Introduction ReIDentification Motivation The Transitive Alg. Experiments Summary & Future Work Camera A F F same C C Camera B not same F F * Image from: L. Bazzani, M. Cristani, and V. Murino. Symmetry-driven accumulation of local features for human characterization and re- identification.
Introduction Motivation Motivation The Transitive Alg. Experiments Summary & Future Work ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ??
Introduction Motivation Motivation The Transitive Alg. Experiments Summary & Future Work By applying the transitive algorithm: By recursively applying the transitive algorithm: ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? non-directly trainable pairs directly trainable pairs
Introduction Motivation Motivation The Transitive Alg. Experiments Summary & Future Work ?? ?? ?? ?? ?? ?? ??+?
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work ?? A ?? ?? ?? B ?? ?? ?? ?? C ??
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work A ??? B ??? C Training Test A B B C A C = ? = ? = ?
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work A B 100 90 ICT C 80 Recognition % 70 SDALF 60 50 Naive ICT 40 30 ICT trained on 3-7 data SDALF for cameras 3-7 ICT trained on 3-5 and 5-7 data 20 10 1 2 3 4 5 6 7 8 9 10 11 12 rank score
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work A ??? ?)} B ?,?? ?)} ???= {(?? ?,?? ???= {(?? ?, ? = ? ?, ? = ? ??? ?) = ?,?? ?) = ???(?? ?,?? ???(?? ?, ? ? ?, ? ? C ?(???|??,??) ?(???|??,??) ? ?????,?? =? ? ?????,?? = ? ???,???= ???,???= ???,?? ??,??)??? ??? {?,?} ??? {?,?} ?? ?? ? ?????,?? = ? ???,???= ???,???= ?????,?? ,??)???(??)??? ??? {?,?} ??? {?,?} ?? ??
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work A ?)} B ?,?? ?)} ???= {(?? ?,?? ???= {(?? ?, ? = ? ?, ? = ? ?) = ?,?? ?) = ???(?? ?,?? ???(?? ?, ? ? ?, ? ? C ?(???|??,??) ?(???|??,??) ? ?????,?? =? .... ? ?????,??? ?????,?? ????? ??? ? ?? ? ?????,??? ?????,?? ????? ??? ?? ? ?????,?? =
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work A B ? ?????,??? ?????,?? ????? ??? ? ?? ? ?????,??? ?????,?? ????? ??? ?? ? ?????,?? = C
Introduction The Transitive Algorithm Motivation The Transitive Alg. Experiments Summary & Future Work A B ? ?????,??? ?????,?? ????? ??? ? ?? ? ?????,??? ?????,?? ????? ??? ?? ? ?????,?? = C ? ?? ?? ? ?????,??? ?????,?? ?? ? ?????,?? ? ?? ?? ? ?????,??? ?????,?? ? ??
Introduction Synthetic Experiment 1 Motivation The Transitive Alg. Experiments Summary & Future Work A ?? ?? ?? ?? ?? ?? B 50 50 50 50 50 50 50 50 20 20 20 20 0 0 0 0 0 0 0 0 0 0 0 0 -20 -20 -20 -20 -50 -50 -50 -50 -50 -50 -50 -50 C -50 -50 -50 -50 0 0 0 0 50 50 50 50 -40 -20 -40 -20 -40 -20 -40 -20 0 0 0 0 20 20 20 20 40 40 40 40 -50 -50 -50 -50 0 0 0 0 50 50 50 50 100 100 100 100 Recognition % Recognition % Recognition % Recognition % ICT trained on AC data Naive ICT Naive ICT ICT trained on AC data ICT trained on AC data Naive ICT TRID TRID TRID Naive ICT TRID ICT trained on AC data 50 50 50 50 0 0 0 0 rank score score score rank score rank rank 0 0 0 0 20 20 20 20 40 40 40 40 60 60 60 60 80 80 80 80 100 100 100 100 ?? ?? ?? ?? ?? ?? ? ?????,??: ????? ??? ??? ??????? ?? ?? ???? ????
Introduction Synthetic Experiment 2 Motivation The Transitive Alg. Experiments Summary & Future Work A ?? ?? ?? ?? ?? ?? B 20 20 50 50 50 50 20 0 0 50 50 0 0 0 0 0 -20 -20 0 0 -50 -50 -50 -50 C -20 -50 -50 0 0 50 50 -20 -20 0 0 20 20 -50 -50 0 0 50 50 -50 -50 (d) (d) -50 0 50 -20 0 20 -50 0 50 100 100 Recognition % Recognition % (d) 100 Recognition % ICT trained on AC data Naive ICT ICT trained on AC data Naive ICT TRID TRID 50 50 ICT trained on AC data Naive ICT TRID 80 90 80 90 50 0 0 rank rank score score 0 0 10 10 20 20 30 30 40 40 50 50 60 60 70 70 100 100 0 rank score 0 10 ?? ?? 20 30 40 50 60 70 80 90 100 ?? ?? ?? ?? ? ?????,??: ????? ??? ??? ??????? ?? ?? ???? ????
Introduction SAIVT-SoftBio Experiment Motivation The Transitive Alg. Experiments Summary & Future Work image from: A. Bialkowski, S. Denman, P. Lucey, S. Sridharan, and C. B. Fookes. A database for person re-identification in multi-camera surveillance networks. (DICTA 2012)
Introduction SAIVT-SoftBio Experiment Motivation The Transitive Alg. Experiments Summary & Future Work C5 C7 C3 [A B C] = C5 C7 C1 [A B C] = 100 100 90 90 80 80 Recognition % Recognition % 70 70 60 60 50 50 ICT trained on AC data Naive ICT TRID SDALF 40 40 ICT trained on AC data Naive ICT TRID SDALF 30 30 20 20 0 10 0 10 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 rank score rank score C3 C8 C1 [A B C] = C3 C1 C5 [A B C] = 100 100 90 90 80 80 Recognition % Recognition % 70 70 60 60 50 50 ICT trained on AC data Naive ICT TRID SDALF ICT trained on AC data Naive ICT TRID SDALF 40 40 30 30 20 20 0 0 10 10 2 4 6 8 10 12 14 5 10 15 rank score rank score
Introduction SAIVT-SoftBio Experiment Motivation The Transitive Alg. Experiments Summary & Future Work C5 C7 C3 [A B C] = 100 90 80 Recognition % 70 60 50 40 ICT trained on AC data Naive ICT TRID SDALF 30 20 0 10 2 4 6 8 10 12 rank score B A C
Introduction SAIVT-SoftBio Experiment Motivation The Transitive Alg. Experiments Summary & Future Work C5 C7 C3 [A B C] = C5 C7 C1 [A B C] = 100 100 90 90 80 80 Recognition % Recognition % 70 70 60 60 50 50 ICT trained on AC data Naive ICT TRID SDALF 40 40 ICT trained on AC data Naive ICT TRID SDALF 30 30 20 20 0 10 0 10 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 rank score rank score C3 C8 C1 [A B C] = C3 C1 C5 [A B C] = 100 100 90 90 80 80 Recognition % Recognition % 70 70 60 60 50 50 ICT trained on AC data Naive ICT TRID SDALF ICT trained on AC data Naive ICT TRID SDALF 40 40 30 30 20 20 0 0 10 10 2 4 6 8 10 12 14 5 10 15 rank score rank score
Introduction Motivation The Transitive Alg. Experiments Summary & Future Work SAIVT-SoftBio Experiment A B C C3 C8 [A B C] = C1 100 90 80 Recognition % 70 60 50 ICT trained on AC data Naive ICT TRID SDALF 40 30 20 0 10 5 10 15 rank score
Introduction Summary Motivation The Transitive Alg. Experiments Summary & Future Work
Introduction Future Work Motivation The Transitive Alg. Experiments Summary & Future Work ?? ?? ?? ??
Introduction Thank-You. Motivation The Transitive Alg. Experiments Summary & Future Work
