Update readme

README.md

@@ -52,6 +52,32 @@ For using the opencv `dnn`-based object detection modules provided in this repos
 Please refer [examples](https://github.com/adipandas/multi-object-tracker/tree/master/examples) folder of this repository.
 You can clone and run the examples as shown [here](examples/readme.md).
 
+The interface for each tracker is simple and similar.
+
+```
+from mottrackers import CentroidTracker # IOUTracker, CentroidKF_Tracker, SORT
+
+input_data = ...
+detector = ...
+tracker = CentroidTracker(...)
+
+while True:
+    done, image = <read(input_data)>
+    if done:
+        break
+
+    detection_bboxes, detection_confidences, detection_class_ids = detector.detect(image)
+
+    output_tracks = tracker.track(detection_bboxes, detection_confidences, detection_class_ids)
+
+    # `output_tracks` is a list with each element containing tuple of
+    # (<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>)
+    for track in output_tracks:
+        frame, id, bb_left, bb_top, bb_width, bb_height, confidence, x, y, z = track
+        assert len(track) == 10
+        print(track)
+```
+
 ## Pretrained object detection models
 
 You will have to download the pretrained weights for the neural-network models. 
@@ -62,9 +88,9 @@ Please refer [DOWNLOAD_WEIGHTS.md](DOWNLOAD_WEIGHTS.md) for more details.
 * There are some variations in implementations as compared to what appeared in papers of `SORT` and `IoU Tracker`.
 * In case you find any bugs in the algorithm, I will be happy to accept your pull request or you can create an issue to point it out.
 
-## References and Credits
+## References, Credits and Contributions
 
-Please see [REFERENCES.md](REFERENCES.md).
+Please see [REFERENCES.md](docs/readme/REFERENCES.md) and [CONTRIBUTING.md](docs/readme/CONTRIBUTING.md).
 
 ## Citation
 

REFERENCES.md → docs/readme/REFERENCES.md

@@ -9,5 +9,6 @@ This work is based on the following literature:
 2. Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016, September). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3464-3468). IEEE. [[arxiv](https://arxiv.org/abs/1602.00763)]
 3. YOLOv3. [[pdf](https://pjreddie.com/media/files/papers/YOLOv3.pdf)][[website](https://pjreddie.com/darknet/yolo/)]
 4. Kalman Filter. [[wiki](https://en.wikipedia.org/wiki/Kalman_filter)]
-5. TensorFlow Object Detection API [[github](https://github.com/tensorflow/models/tree/master/research/object_detection)]
-6. Caffe [[website](https://caffe.berkeleyvision.org/)][[github](https://github.com/BVLC/caffe)]
+5. TensorFlow Object Detection API [[GitHub](https://github.com/tensorflow/models/tree/master/research/object_detection)]
+6. Caffe [[website](https://caffe.berkeleyvision.org/)][[GitHub](https://github.com/BVLC/caffe)]
+7. OpenCV. [[GitHub](https://github.com/opencv/opencv)] [[Website](https://opencv.org/)]

examples/example_scripts/readme.md

@@ -0,0 +1,7 @@
+## How to use?
+
+To see how to use these example scripts, simply type the following in the terminal:
+
+```
+python3 <file-name.py> --help
+```

examples/motmetrics_eval/readme.md

@@ -1,7 +1,7 @@
 ### MOT Challenge file format
 
-[GitHub](https://github.com/adipandas/multi-object-tracker)
-[Home](https://adipandas.github.io/multi-object-tracker/)
+[[GitHub](https://github.com/adipandas/multi-object-tracker)]
+[[Home](https://adipandas.github.io/multi-object-tracker/)]
 
 The file format should be the same as the ground truth file, which is a CSV text-file containing one object instance per line.
 

requirements.txt

@@ -1,7 +1,7 @@
 numpy
 scipy
 matplotlib
-pandas
 opencv-contrib-python
+pandas
 motmetrics
 setuptools