CVPR 2019 Detection Challenge Results
Click on a measure to sort the table accordingly. See below for a more detailed description.
| Tracker | AP | MODA | MODP | FAF | TP | FP | FN | Rcll | Prcn | F1 | Hz |
| SRK_ODESA 1. | 0.81 | 75.0 | 79.4 | 12.3 | 340,612 | 55,251 | 39,930 | 89.5 | 86.0 | 87.7 | 3.0 |
| D. Borysenko, D. Mykheievskyi, V. Porokhonskyy. ODESA: Object Descriptor that is Smooth Appearance-wise for object tracking tasks. In (to be submitted to ECCV'20), . | |||||||||||
| ViPeD_19 2. | 0.73 | 39.0 | 71.5 | 35.7 | 308,545 | 159,983 | 71,997 | 81.1 | 65.9 | 72.7 | 11.2 |
| G. Amato, L. Ciampi, F. Falchi, C. Gennaro, N. Messina. Learning pedestrian detection from virtual worlds. In International Conference on Image Analysis and Processing, 2019. | |||||||||||
| Sequences | Frames | Trajectories | Boxes |
| 4 | 4479 | 1492 | 803370 |
Evaluation Measures
Lower is better. Higher is better.| Measure | Better | Perfect | Description |
| AP | higher | 1 | Average Precision taken over a set of reference recall values (0:0.1:1) |
| MODA | higher | 100% | Multi-Object Detection Accuracy [1]. This measure combines false positives and missed targets. |
| MODP | higher | 100% | Multi-Object Detection Precision [1]. The misalignment between the annotated and the predicted bounding boxes. |
| FAF | lower | 0 | The average number of false alarms per frame. |
| TP | higher | #GT | The total number of true positives. |
| FP | lower | 0 | The total number of false positives. |
| FN | lower | 0 | The total number of false negatives (missed targets). |
| Rcll | higher | 100% | Ratio of correct detections to total number of GT boxes. |
| Prcn | higher | 100% | Ratio of TP / (TP+FP). |
| F1 | higher | 100% | Harmonic mean of precision and recall. |
| Hz | higher | Inf. | Processing speed (in frames per second excluding the detector) on the benchmark. The frequency is provided by the authors and not officially evaluated by the MOTChallenge. |
Legend
| Symbol | Description |
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This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time. |
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This method used the provided detection set as input. |
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This method used a private detection set as input. |
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This entry has been submitted or updated less than a week ago. |
References:
| [1] | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008. |
