motchallenge-devkit.zip

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  • 2022-06-08 17:10
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基于MATLAB的MOT官方评估工具,解压即可使用,亲测有效,童叟无欺。(我博客里也有使用步骤)
motchallenge-devkit.zip
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Multiple Object Tracking Challenge Development Kit ================================================== [MOTChallenge.net](https://motchallenge.net) Description =========== This development kit provides scripts to evaluate detection/tracking results. Please report bugs to the authors: Anton Milan - anton.milan@adelaide.edu.au Ergys Ristani - ristani@cs.duke.edu Requirements ============ - MATLAB - C/C++ compiler - Benchmark data for MOT15-17 e.g. 2DMOT2015, available here: http://motchallenge.net/data/2D_MOT_2015/ - Note 1: DukeMTMCT benchmark data will download automatically. - Note 2: The code has been tested under Windows and Linux. Usage ===== 1) Run: compile 2) Run one of the following: demo_evalMOT15 demo_evalMOT15_3D demo_evalMOT16 demo_evalMOT17Det demo_evalDukeMTMCT Note: For demo_evalMOT1X you need to replace the benchmarkGtDir path to point to the training set data. For example: benchmarkGtDir = '../data/2DMOT2015/train/'; allMets = evaluateTracking('c2-train.txt', 'res/MOT15/data/', benchmarkGtDir, 'MOT15'); For detector evaluation: benchmarkDir = '../data/MOT17Det/train/'; evaluateDetection('c9-train.txt', 'res/MOT17Det/DPM/data', benchmarkDir); You should see the following outputs: >> demo_evalMOT15 Sequences: 'TUD-Stadtmitte' 'TUD-Campus' 'PETS09-S2L1' 'ETH-Bahnhof' 'ETH-Sunnyday' 'ETH-Pedcross2' 'ADL-Rundle-6' 'ADL-Rundle-8' 'KITTI-13' 'KITTI-17' 'Venice-2' ... TUD-Stadtmitte TUD-Stadtmitte IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL 64.5 82.0 53.1| 60.9 94.0 0.25| 10 5 4 1| 45 452 7 6| 56.4 65.4 56.9 ... TUD-Campus TUD-Campus IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL 55.8 73.0 45.1| 58.2 94.1 0.18| 8 1 6 1| 13 150 7 7| 52.6 72.3 54.3 ... ... Venice-2 Venice-2 IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL 35.5 43.6 29.9| 42.0 61.3 3.15| 26 4 16 6| 1890 4144 42 52| 14.9 72.6 15.5 ********************* Your 2DMOT15 Results ********************* IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL 41.2 53.2 33.6| 45.3 71.7 1.30|500 81 161 258| 7129 21842 220 338| 26.8 72.4 27.4 For detector evaluation you should see: >> demo_evalMOT17Det Challenge: MOT17Det Set: Training Set Sequences: 'MOT17-02' 'MOT17-04' 'MOT17-05' 'MOT17-09' 'MOT17-10' 'MOT17-11' 'MOT17-13' Preprocessing (cleaning) MOT17-02... ...... Removing 1074 boxes from solution... Preprocessing (cleaning) MOT17-04... .......... ... Evaluating unknown ... MOT17-02 ... MOT17-04 ... MOT17-05 ... MOT17-09 ... MOT17-10 ... MOT17-11 ... MOT17-13 Ok, results are valid. EVALUATING... *** Dataset: MOT17Det *** Recall: 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Precision: 1.000 1.000 0.999 0.995 0.975 0.913 0.748 0.000 0.000 0.000 0.000 Average Precision: 0.6027 Rcll Prcn| FAR GT TP FP FN| MODA MODP 64.7 60.2| 5.34 66393 42979 28405 23414| 22.0 77.0 Here are the per-sequence evaluations: ... MOT17-02 Recall: 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Precision: 1.000 0.999 0.990 0.971 0.892 0.774 0.000 0.000 0.000 0.000 0.000 Average Precision: 0.5115 Rcll Prcn| FAR GT TP FP FN| MODA MODP 58.0 68.3| 3.27 7288 4230 1963 3058| 31.1 75.3 ... MOT17-04 Recall: 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Precision: 1.000 1.000 1.000 1.000 0.992 0.960 0.885 0.664 0.000 0.000 0.000 Average Precision: 0.6818 Rcll Prcn| FAR GT TP FP FN| MODA MODP 72.2 58.1|14.36 28936 20891 15077 8045| 20.1 78.4 ... ... MOT17-13 Recall: 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Precision: 1.000 0.968 0.910 0.726 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Average Precision: 0.3275 Rcll Prcn| FAR GT TP FP FN| MODA MODP 34.7 56.5| 2.87 8039 2788 2150 5251| 7.9 73.4 Details ======= evaluateTracking(seqmap, resDir, gtDataDir, benchmark) The tracking evaluation script accepts 4 arguments: 1) seqmap sequence map (e.g. `c2-train.txt`) contains a list of all sequences to be evaluated in a single run. These files are inside the ./seqmaps folder. 2) resDir The folder containing the tracking results. Each one should be saved in a separate .txt file with the name of the respective sequence (see ./res/data) 3) gtDataDir The folder containing the ground truth files. 4) benchmark The name of the benchmark, e.g. 'MOT15', 'MOT16', 'MOT17', 'DukeMTMCT' The results will be shown for each individual sequence as well as for the entire benchmark. Benchmark scores are aggregate scores for all sequences.     evaluateDetection(seqmap, resDir, gtDataDir) The detection evaluation script accepts 3 arguments: 1) seqmap sequence map (e.g. `c9-train.txt`) contains a list of all sequences to be evaluated in a single run. These files are inside the ./seqmaps folder. 2) resDir The folder containing the detection results. Each one should be saved in a separate .txt file with the name of the respective sequence (see ./res/data) 3) gtDataDir The folder containing the ground truth files. Directory structure =================== ./res ---------- This directory contains - the tracking results for each sequence in a subfolder data - eval.txt, which shows all metrics for this demo ./utils ------- Various scripts and functions used for evaluation. ./seqmaps --------- Sequence lists for different benchmarks Version history =============== 1.4 - Sep 30, 2017 - Bitbucket release 1.3 - Apr 29, 2017 - Merged single- and multi-camera evaluation branches - Code cleanup - Evaluation code ported to C++ 1.2 - Apr 16, 2017 - Included evaluation for detections - Made evaluation script more efficient 1.1.1 - Oct 10, 2016 - Included camera projections scripts 1.1 - Feb 25, 2016 - Included evaluation for the new MOT16 benchmark 1.0.5 - Nov 10, 2015 - Fixed bug where result has only one frame - Fixed bug where results have extreme values for IDs - Results may now contain invalid frames, IDs, which will be ignored 1.0.4 - Oct 08, 2015 - Fixed bug where result has more frames than ground truth 1.0.3 - Jul 04, 2015 - Removed spurious frames from ETH-Pedcross2 result (thanks Nikos) 1.0.2 - Mar 11, 2015 - Fix to exclude small bounding boxes from ground truth - Special case of empty mapping fixed 1.0.1 - Feb 06, 2015 - Fixes in 3D evaluation (thanks Michael) 1.00 - Jan 23, 2015 - initial release
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