V-IOU results and Mask R-CNN detections added

bochinski · bochinski · commit abe18056950a · 2018-12-19T13:57:06.000+01:00
diff --git a/README.md b/README.md
@@ -15,9 +15,20 @@ If you think our work is useful in your research, please consider citing:
 	ADDRESS = {Lecce, Italy},
 	URL = {http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf},
 	}
+
+@INPROCEEDINGS{1547Bochinski2018,
+	AUTHOR = {Erik Bochinski and Tobias Senst and Thomas Sikora},
+	TITLE = {Extending IOU Based Multi-Object Tracking by Visual Information},
+	BOOKTITLE = {IEEE International Conference on Advanced Video and Signals-based Surveillance},
+	YEAR = {2018},
+	MONTH = nov,
+	PAGES = {441--446},
+}
 ```
 
 **Update** (December 2018):
+* added V-IOU results of our new paper [Extending IOU Based Multi-Object Tracking by Visual Information](http://elvera.nue.tu-berlin.de/files/1547Bochinski2018.pdf)
+* Mask R-CNN detections for UA-DETRAC added
 * CompACT parameters improved
 
 ## Demo
@@ -56,7 +67,7 @@ Example for the MOT17-04 sequence (detections can be downloaded [here](https://m
 
 ### DETRAC
 To reproduce the reported results, download and extract the [DETRAC-toolkit](http://detrac-db.rit.albany.edu/download)
-and the detections you want to evaluate. Download links for the EB detections are provided below.
+and the detections you want to evaluate. Download links for the EB and Mask R-CNN detections are provided below.
 Clone this repository into "DETRAC-MOT-toolkit/trackers/".
 Follow the instructions to configure the toolkit for tracking evaluation and set the tracker name in "DETRAC_experiment.m":
 
@@ -70,27 +81,32 @@ Note that you still need a working python environment with numpy installed.
 You should obtain something like the following results for the 'DETRAC-Train' set:
 
 ##### DETRAC-Train Results
-| Detector | PR-Rcll | PR-Prcn | PR-FAR | PR-MT | PR-PT  | PR-ML | PR-FP   | PR-FN   | PR-IDs| PR-FM | PR-MOTA | PR-MOTP | PR-MOTAL |
-| -------- | ------- | ------- | ------ | ----- | ------ | ----- | ------- | ------- | ----- | ----- | ------- | ------- | -------- |
-| EB       |37.86    |44.73    |0.10    |32.34  |12.88   |20.93  |7958.82  |163739.85|4129.40|4221.89|35.77    |40.81    |36.48     |
-| R-CNN    |27.86    |52.90    |0.11    |19.53  |17.03   |18.56  |9047.95  |157521.18|4842.18|4969.57|25.46    |44.39    |26.29     |
-| CompACT  |25.20    |49.69    |0.10    |18.50  |14.11   |19.06  |8053.54  |153026.99|2021.84|2302.83|23.46    |42.96    |23.81     |
-| ACF      |27.39    |52.68    |0.14    |20.24  |15.66   |19.40  |11553.49 |161293.27|1845.49|2101.44|25.07    |44.71    |25.39     |
+| Detector   | PR-Rcll | PR-Prcn | PR-FAR | PR-MT | PR-PT  | PR-ML | PR-FP   | PR-FN   | PR-IDs| PR-FM | PR-MOTA | PR-MOTP | PR-MOTAL |
+| --------   | ------- | ------- | ------ | ----- | ------ | ----- | ------- | ------- | ----- | ----- | ------- | ------- | -------- |
+| EB         |37.86    |44.73    |0.10    |32.34  |12.88   |20.93  |7958.82  |163739.85|4129.40|4221.89|35.77    |40.81    |36.48     |
+| R-CNN      |27.86    |52.90    |0.11    |19.53  |17.03   |18.56  |9047.95  |157521.18|4842.18|4969.57|25.46    |44.39    |26.29     |
+| CompACT    |25.20    |49.69    |0.10    |18.50  |14.11   |19.06  |8053.54  |153026.99|2021.84|2302.83|23.46    |42.96    |23.81     |
+| ACF        |27.39    |52.68    |0.14    |20.24  |15.66   |19.40  |11553.49 |161293.27|1845.49|2101.44|25.07    |44.71    |25.39     |
+| Mask R-CNN |43.21    |47.26    |0.60    |37.22  |11.46   |24.24  |50096.88 |171714.09|1021.94|929.53 |34.36    |45.43    |34.54     |
 
 ##### DETRAC-Test (Overall) Results
 The reference results are taken from the [UA-DETRAC results](http://detrac-db.rit.albany.edu/TraRet) site. Only the best tracker / detector
 combination is displayed for each reference method.
 
-| Tracker       | Detector | PR-MOTA | PR-MOTP     | PR-MT     | PR-ML     | PR-IDs   | PR-FM    | PR-FP      | PR-FN      | Speed          |
-| ------------- | -------- | ------- | ----------- | --------- | --------- | -------- | -------- | ---------- | ---------- | -------------- |
-|CEM            | CompACT  | 5.1\%     |35.2\%     |3.0\%      |35.3\%     |**267.9** |**352.3** |**12341.2** |260390.4    |4.62 fps        |
-|CMOT           | CompACT  | 12.6\%    |36.1\%     |16.1\%     |18.6\%     |285.3     |1516.8    |57885.9     |**167110.8**| & 3.79 fps     |
-|GOG            | CompACT  | 14.2\%    |37.0\%     |13.9\%     |19.9\%     |3334.6    |3172.4    |32092.9     |180183.8    |390 fps         |
-|DCT            | R-CNN    | 11.7\%    |38.0\%     |10.1\%     |22.8\%     |758.7     |742.9     |336561.2    |210855.6    |0.71 fps        |
-|H<sup>2</sup>T | CompACT  | 12.4\%    |35.7\%     |14.8\%     |19.4\%     |852.2     |1117.2    |51765.7     |173899.8    | 3.02 fps       |
-|IHTLS          | CompACT  | 11.1\%    |36.8\%     |13.8\%     |19.9\%     |953.6     |3556.9    |53922.3     |180422.3    |19.79 fps       |
-|**IOU**        | R-CNN    |16.0\%     |**38.3\%** |13.8\%     |20.7\%     |5029.4    |5795.7    |22535.1     |193041.9    |**100,840 fps** |
-|**IOU**        | EB       |**19.4\%** |28.9\%     |**17.7\%** |**18.4\%** |2311.3    |2445.9    |14796.5	  |171806.8    |6,902 fps       |
+| Tracker       | Detector    | PR-MOTA | PR-MOTP     | PR-MT     | PR-ML     | PR-IDs   | PR-FM    | PR-FP      | PR-FN      | Speed          |
+| ------------- | ----------- | ------- | ----------- | --------- | --------- | -------- | -------- | ---------- | ---------- | -------------- |
+|CEM            | CompACT     | 5.1\%     |35.2\%     |3.0\%      |35.3\%     |267.9     |352.3     |**12341.2** |260390.4    |4.62 fps        |
+|CMOT           | CompACT     | 12.6\%    |36.1\%     |16.1\%     |18.6\%     |285.3     |1516.8    |57885.9     |167110.8    |3.79 fps        |
+|GOG            | CompACT     | 14.2\%    |37.0\%     |13.9\%     |19.9\%     |3334.6    |3172.4    |32092.9     |180183.8    |390 fps         |
+|DCT            | R-CNN       | 11.7\%    |38.0\%     |10.1\%     |22.8\%     |758.7     |742.9     |336561.2    |210855.6    |0.71 fps        |
+|H<sup>2</sup>T | CompACT     | 12.4\%    |35.7\%     |14.8\%     |19.4\%     |852.2     |1117.2    |51765.7     |173899.8    |3.02 fps        |
+|IHTLS          | CompACT     | 11.1\%    |36.8\%     |13.8\%     |19.9\%     |953.6     |3556.9    |53922.3     |180422.3    |19.79 fps       |
+|**IOU**        | R-CNN    |16.0\%        |**38.3\%** |13.8\%     |20.7\%     |5029.4    |5795.7    |22535.1     |193041.9    |100,840 fps     |
+|**IOU**        | EB       |19.4\%        |28.9\%     |17.7\%     |**18.4\%** |2311.3    |2445.9    |14796.5	  |171806.8   |6,902 fps       |
+|**IOU**        | CompACT     | 16.1\%    |37.0\%     |14.8\%     |19.7\%     |2308.1    |3250.4    |24349.4     |176752.8    |**327,660 fps** |
+|**IOU**        | Mask R-CNN  | **30.7\%**|37.0\%     |30.3\%     |21.5\%     |668.0     |733.6     |17370.3     |179505.9    |14,956 fps      |
+|**V-IOU**      | CompACT     | 17.7\%    |36.4\%     |17.4\%     |18.8\%     |363.8     |1123.5    |26413.3     |**166571.7**|1117.90fps      |
+|**V-IOU**      | Mask R-CNN  | **30.7\%**|37.0\%     |**32.0\%** |22.6\%     |**162.6** |**286.2** |18046.2     |179191.2    |359.18 fps      |
 
 ##### EB detections
 The public detections of [EB](http://zyb.im/research/EB/) are not available on the
@@ -100,6 +116,13 @@ publication are available here:
 * [EB Train](https://tubcloud.tu-berlin.de/s/EtC6cFEYsAU0gFQ/download)
 * [EB Test](https://tubcloud.tu-berlin.de/s/oKM3dYhJbMFl1dY/download)
 
+##### Mask R-CNN detections
+These detections are generated using a recent Mask R-CNN implementation trained on COCO.
+Only bounding boxes for COCOs *car*, *bus* and *truck* classes are included.
+Note that the detector is called "frcnn" (use `options.detectorSet = {'frcnn'};` in *initialize_environment.m*).
+* [Mask R-CNN Train](https://tubcloud.tu-berlin.de/s/MnGRGdH98WY9xQr/download)
+* [Mask R-CNN Test](https://tubcloud.tu-berlin.de/s/EztsFgm5AL8Jwtt/download)
+
 ### MOT17
 The IOU Tracker was evaluated on the MOT17 benchmark as well. To determine the best parameters for each detector, an
 exhaustive search of the parameter space was performed similar to the one of the MOT16 evaluation reported in the paper.
diff --git a/run_tracker.m b/run_tracker.m
@@ -1,10 +1,17 @@
 function [stateInfo, speed] = run_tracker(curSequence, baselinedetections)
 %% tracker configuration
-%% R-CNN
+
+%% Mask R-CNN (frcnn)
 sigma_l = 0;
-sigma_h = 0.7;
-sigma_iou = 0.5;
-t_min = 2;
+sigma_h = 0.95;
+sigma_iou = 0.6;
+t_min = 7;
+
+% %% R-CNN
+% sigma_l = 0;
+% sigma_h = 0.7;
+% sigma_iou = 0.5;
+% t_min = 2;
 
 % %% ACF
 % sigma_l = 0;
