Performance Measures and the DukeMTMC Benchmark for Multi-Target Multi-Camera Tracking. Ergys Ristani
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1 Performance Measures and the DukeMTMC Benchmark for Multi-Target Multi-Camera Tracking Ergys Ristani
2 In collaboration with: Francesco Solera Roger Zou Rita Cucchiara Carlo Tomasi
3 Outline Problem Definition Performance Measures DukeMTMCT Benchmark Summary
4 Problem Definition DukeMTMCT Benchmark Multi-Target Multi-Camera Tracking Given n camera streams, determine who/what is where at all times
5 Problem Definition DukeMTMCT Benchmark Multi-Target Multi-Camera Tracking Given n camera streams, determine who/what is where at all times
6 Problem Definition Multi-Target Multi-Camera Tracking Given n camera streams, determine who/what is where at all times MTMCT Evaluation DukeMTMCT Benchmark How well does a tracker determine who/what is where at all times?
7 Evaluation Paradigms Suspect
8 Evaluation Paradigms Computed IDs ID1 Tracker (a) Suspect
9 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect
10 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch
11 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification
12 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect
13 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification
14 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification Tracker (c) Suspect 7 switches 83% identification
15 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification Tracker (c) Suspect Researcher: How often does the tracker switch identities? 7 switches 83% identification
16 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification Tracker (c) Suspect 7 switches 83% identification
17 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification Tracker (c) Suspect End-user: How well does the tracker explain identity? 7 switches 83% identification
18 Evaluation Paradigms Computed IDs ID1 ID2 Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification Tracker (c) Suspect 7 switches 83% identification
19 Outline Problem Definition Performance Measures Existing Measures Issues Identity Measures DukeMTMCT Benchmark Summary
20 Performance Measures MTMC Tracker Performance
21 Performance Measures Interaction model Object detection Optimization Appearance model Parameter tuning Training data Motion model
22 Performance Measures MTMC Tracker Performance
23 Performance Measures MOTA IDS MTMC Tracker Performance MT ML FRG [1] Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image Video Proc [2] Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. CVPR 2009
24 Performance Measures MOTA IDS MT ML FRG MTMC Tracker Performance MCTA Handover (X, FRG) [3] An Equalized Global Graph Model-Based Approach for Multi-Camera Object Tracking. IEEE TCAS 2016 [4] Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models. ECCV 2010
25 Performance Measures MOTA IDS MT ML FRG MTMC Tracker Performance MCTA Handover (X, FRG)
26 [5] Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol. TPAMI 2008 [6] A New Benchmark and Protocol for Multi-Object Detection and Tracking. arxiv CoRR 2015 [7] Evaluating Multi-Object Tracking. CVPRWS 2005 Performance Measures PR-MOTA MOTA IDS MCTA FIO FIT STDA-D ATA MT ML FRG MTMC Tracker Performance Handover (X, FRG) TP OP
27 Errors in Tracking Mismatches Fragmentations T 1 True Computed C 1 C 2 Merges T 1 T 2 C 1 False Positives/Negatives T 1 C 2
28 Key Evaluation Steps T 1 C 1 C 3 True Computed C 2 T 2
29 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 T 2
30 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2
31 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
32 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
33 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T 2 time
34 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
35 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
36 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
37 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
38 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
39 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
40 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
41 CLEAR MOT Mapping At each frame t Preserve mapping of detections from frame t-1 if still valid Solve bipartite matching at frame t for unmapped detections T 1 C 1 C 3 True Computed C 2 T time
42 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
43 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
44 Key Evaluation Steps Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
45 Common Scoring Functions Multiple Object Tracking Accuracy (MOTA) Multiple Camera Tracking Accuracy (MCTA) Trajectory Scores MT, PT, ML Handover Errors
46 Outline Problem Definition Performance Measures Existing Measures Issues Identity Measures DukeMTMCT Benchmark Summary
47 Issues Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
48 Issues Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
49 CLEAR MOT Mapping Identity mapping bijective at each frame Not bijective overall T 1 C 1 T 2 C 2 T 1 C 1 C 3 True Computed C 2 T 2 time
50 Issues Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
51 Scoring Functions Multiple Object Tracking Accuracy (MOTA) Multiple Camera Tracking Accuracy (MCTA) Trajectory Scores MT, PT, ML Handover Errors
52 Scoring Functions Multiple Object Tracking Accuracy (MOTA) Multiple Camera Tracking Accuracy (MCTA) Trajectory Scores MT, PT, ML Handover Errors Brittle due to mapping
53 Handover Errors Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A
54 Handover Errors Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b time
55 Handover Errors Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b time
56 Handover Errors Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b Tracker 2 b b b b b b b b b b b b b b b time
57 Handover Errors Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b Tracker 2 b b b b b b b b b b b b b b b time
58 Scoring Functions Multiple Object Tracking Accuracy (MOTA) Multiple Camera Tracking Accuracy (MCTA) Trajectory Scores MT, PT, ML Ignore identity Handover Errors
59 Trajectory Scores Truth A A A A A A A A A A time
60 Trajectory Scores Truth A A A A A A A A A A time Mostly Tracked: GT trajectories which are covered by tracker output for more than 80% in length
61 Trajectory Scores Truth A A A A A A A A A A Tracker 1 b b c c e e g g f f time Mostly Tracked: GT trajectories which are covered by tracker output for more than 80% in length
62 Trajectory Scores Truth A A A A A A A A A A Tracker 1 b b c c e e g g f f MT = 1 time Mostly Tracked: GT trajectories which are covered by tracker output for more than 80% in length
63 Trajectory Scores Truth A A A A A A A A A A Tracker b b c c b g g g b b MT = 1 FRG = 4 time Mostly Tracked: Percentage of GT trajectories which are covered by tracker output for more than 80% in length Fragments: The number of times that a GT trajectory is interrupted in tracking result
64 Trajectory Scores Truth A A A A A A A A A A Tracker b b c c e e g g f f MT = 1 FRG = 4 time
65 Trajectory Scores Truth A A A A A A A A A A Tracker b b c c e e g g f f MT = 1 FRG = 4 Tracker++ b b c b b g b g b b time
66 Trajectory Scores Truth A A A A A A A A A A Tracker b b c c e e g g f f MT = 1 FRG = 4 Tracker++ b b c b b g b g b b MT = 1 FRG = 4 time
67 Scoring Functions Multiple Object Tracking Accuracy (MOTA) Multiple Camera Tracking Accuracy (MCTA) Bizarre combination Trajectory Scores MT, PT, ML Handover Errors
68 MCTA Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b Tracker 2 b b b b b b c b b b b b b b b b time
69 MCTA Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b Tracker 2 b b b b b b c b b b b b b b b b time Tracker 1 Fragmentations Handover Within camera Tracker 2
70 MCTA Camera 1 Camera 2 Truth A A A A A A A A A A A A A A A A Tracker 1 b b b b b b b c b b b b b b b b Tracker 2 b b b b b b c b b b b b b b b b time Tracker 1 Fragmentations Handover Within camera Tracker 2
71 Scoring Functions Multiple Object Tracking Accuracy (MOTA) Multiple Camera Tracking Accuracy (MCTA) Mapping not one-to-one Trajectory Scores MT, PT, ML Handover Errors
72 MOTA True Computed A A A A A A A A b b b b c c c c time True A A A A A A A A Computed b b b b b b b c time
73 MOTA True Computed A A A A A A A A b b b b c c c c Half explanation time True A A A A A A A A Computed b b b b b b b c Nearly full explanation time
74 Outline Problem Definition Performance Measures Existing Measures Issues Identity Measures DukeMTMCT Benchmark Summary
75 Identity Measures Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
76 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time
77 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time A b c
78 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time A b c Cost on each edge is number of mis-assigned frames between two trajectories
79 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b b b b b b b time A 4 b Cost on each edge is number of mis-assigned frames between two trajectories
80 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time A b c Cost on each edge is number of mis-assigned frames between two trajectories
81 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True Computed A A A A A A A A A A A A A A A A c c time A 14 c Cost on each edge is number of mis-assigned frames between two trajectories
82 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time A b c Cost on each edge is number of mis-assigned frames between two trajectories
83 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True Computed A A A A A A A A A A A A A A A A time A 16 Cost on each edge is number of mis-assigned frames between two trajectories
84 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time A b c Cost on each edge is number of mis-assigned frames between two trajectories
85 Identity Measures Proposed Truth-to-Result Matching True Camera 1 Camera 2 Computed b b b b b b b b b b b b time b 12 Cost on each edge is number of mis-assigned frames between two trajectories
86 Identity Measures Proposed Truth-to-Result Matching Camera 1 Camera 2 True A A A A A A A A A A A A A A A A Computed b b b b b b c c b b b b b b time A b c Cost on each edge is number of mis-assigned frames between two trajectories
87 Identity Measures Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
88 Identity Measures Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
89 Identity Measures Truth-to-Result Matching T 1 C 1 C 3 True Computed C 2 Scoring Function T 2 PR-MOTA STDA-D ATA MOTA IDS MT ML MTMC Tracker Performance FRG MCTA Handover (X, FRG) FIO FIT OP TP
90 Identity Measures Proposed Scores ID Precision ID Recall F 1 -score False Negatives True Positives False Positives True Detections Computed Detections
91 Identity Measures Proposed Scores IDP: Precision Fraction of computed detections that are correctly identified. ID Recall F 1 -score False Negatives True Positives False Positives True Detections Computed Detections
92 Identity Measures Proposed Scores IDP: Precision Fraction of computed detections that are correctly identified. IDR: Recall Fraction of ground-truth detections that are correctly identified. F 1 -score False Negatives True Positives False Positives True Detections Computed Detections
93 Identity Measures Proposed Scores IDP: Precision Fraction of computed detections that are correctly identified. IDR: Recall Fraction of ground-truth detections that are correctly identified. FIDF1: 1 -score Ratio of correctly identified detections over the average number of ground-truth and computed detections False Negatives True Positives False Positives True Detections Computed Detections
94 Identity Measures Properties True and computed identities are mapped 1-1 Notion of identity switch is not present in ID measures The truth-to-result matching is the most favorable to the algorithm Applicable to single- and multi-camera tracking
95 Identity Measures Properties True and computed identities are mapped 1-1 Notion of identity switch is not present in ID measures The truth-to-result matching is the most favorable to the algorithm Applicable to single- and multi-camera tracking A b c
96 Identity Measures Properties True and computed identities are mapped 1-1 Notion of identity switch is not present in ID measures The truth-to-result matching is the most favorable to the algorithm Applicable to single- and multi-camera tracking A b A b c c
97 Identity Measures Properties True and computed identities are mapped 1-1 Notion of identity switch is not present in ID measures The truth-to-result matching is the most favorable to the algorithm Applicable to single- and multi-camera tracking A b c A b c Min Cost Matching
98 Identity Measures Properties True and computed identities are mapped 1-1 Notion of identity switch is not present in ID measures The truth-to-result matching is the most favorable to the algorithm Applicable to single- and multi-camera tracking T 1 C 1 C 3 C 2 T 2
99 Identity Measures Properties True and computed identities are mapped 1-1 Notion of identity switch is not present in ID measures The truth-to-result matching is the most favorable to the algorithm Applicable to single- and multi-camera tracking T 1 C 1 C 3 C 2 T 2
100 Scope and Limitations ID measures are useful in: Surveillance Sport MTT for autonomous driving Not necessary when: Tracking indistinguishable targets (ants, sheep) Not applicable to: Tracking targets that merge/split (cells)
101 Practical Implications MOT 16 Challenge Tracker MOTA HCC LMP FWT NLLMPa MDPNN NOMT_ JMC QuadMOT oicf_ MHT_DAM_ LINF1_ EAMTT_pub OVBT LTTSC-CRF LP2D_ TBD_ CEM_ DP_NMS_ GMPHD_HDA SMOT_ JPDA_m_
102 Practical Implications MOT 16 Challenge Tracker MOTA HCC LMP FWT NLLMPa MDPNN NOMT_ JMC QuadMOT oicf_ MHT_DAM_ LINF1_ EAMTT_pub OVBT LTTSC-CRF LP2D_ TBD_ CEM_ DP_NMS_ GMPHD_HDA SMOT_ JPDA_m_ Tracker IDF1 NOMT_ LMP HCC oicf_ NLLMPa JMC MDPNN MHT_DAM_ LINF1_ FWT EAMTT_pub LTTSC-CRF QuadMOT OVBT CEM_ LP2D_ GMPHD_HDA JPDA_m_ DP_NMS_ TBD_ SMOT_
103 Practical Implications MOT 16 Challenge Tracker MOTA HCC LMP FWT NLLMPa MDPNN NOMT_ JMC QuadMOT oicf_ MHT_DAM_ LINF1_ EAMTT_pub OVBT LTTSC-CRF LP2D_ TBD_ CEM_ DP_NMS_ GMPHD_HDA SMOT_ JPDA_m_ Tracker IDF1 NOMT_ LMP HCC oicf_ NLLMPa JMC MDPNN MHT_DAM_ LINF1_ FWT EAMTT_pub LTTSC-CRF QuadMOT OVBT CEM_ LP2D_ GMPHD_HDA JPDA_m_ DP_NMS_ TBD_ SMOT_ Rank Improvement
104 IDP/IDR Curves Public detections Private detections KDNT DeepSORT POI NOMTwSDP FWT LMP HCC NLLMPa JMC NOMT oicf CEM JPDA
105 Performance Measures IDF1 IDP IDR PR-MOTA MOTA IDS MCTA FIO FIT STDA-D ATA MT ML FRG MTMC Tracker Performance Handover (X, FRG) TP OP [8] Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCV 2016 BMTT Workshop
106 Outline Problem Definition Performance Measures Existing Measures Issues Identity Measures DukeMTMCT Benchmark Summary
107 Previous MTMC Data Sets NLPR 1, 2 EPFL NLPR 3 APIDIS ISSIA PETS2009
108 DukeMTMC 8 static cameras x 85 minutes of 1080p 60 fps video More than 2,000,000 manually annotated frames More than 2,000 identities Annotation by 5 people over 1 year More identities than all existing datasets combined Unconstrained paths, diverse appearance
109
110 Evaluation Single Camera Multi-Camera Ranking IDF1 score
111 Trainval 0-50 min Test-hard min Test-easy min
112 Baseline
113 Baseline: BIPCC Camera i Person detection Multi-Camera Identities MTMC Tracking as Correlation Clustering Tracking Multiple People Online and in Real Time, E. Ristani and C. Tomasi, ACCV 2014
114 Baseline
115 Baseline Incorrect Matches
116 Baseline Correct Matches
117 DukeMTMCT Fragmentations Merges IDR IDP
118 DukeMTMCT Fragmentations Merges IDR IDP ID switches in CLEAR MOT don t correlate with ID measures
119 Outline Problem Definition Performance Measures DukeMTMCT Benchmark Summary
120 Summary MTMC trackers are complex - multiple uncorrelated measures are required to understand tracker performance PR-MOTA STDA-D MOTA IDS IDF1 IDP IDR Interaction model Optimization Object detection Appearance model MTMC Tracker Performance MCTA FIO FIT TP ATA MT ML Parameter tuning FRG Motion model Training data Handover (X, FRG) OP
121 Summary Large-scale realistic benchmarks make comparisons meaningful
122 Summary Different applications require different measures ID measures show bottom line performance: They are useful for end users of MTMC tracking systems Tracker (a) Suspect 1 switch 67% identification Tracker (b) Suspect 7 switches 67% identification Tracker (c) Suspect 83% identification
123 Thank you! Code/Data: vision.cs.duke.edu/dukemtmc/ [1] Performance Measures and Dataset for Multi-Target, Multi-Camera Tracking Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, Carlo Tomasi ECCV 2016 Workshop on Benchmarking Multi-Target Tracking [2] Tracking Social Groups Within and Across Cameras Francesco Solera, Simone Calderara, Ergys Ristani, Carlo Tomasi, Rita Cucchiara IEEE Transactions on Circuits and Systems 2016 [3] Tracking Multiple People Online and in Real Time Ergys Ristani and Carlo Tomasi ACCV 2014
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