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➤ Week 4

During the fourth week of our project, we focused on estimating the speed of vehicles using visual cues and multi-camera tracking.

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➤ Available tasks

  • Task 1: Estimate the speed of vehicles using visual cues
    • Task 1.1: Speed estimation
    • Task 1.2: Speed estimation with our data
  • Task 2: Multi-camera tracking

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➤ Usage

Task 1

python tracking_speed.py [--store] [--vizualize] [--o_name OUTPUT_NAME] [--detections DETECTIONS_FILE]

Mandatory Arguments:

  • --store: This flag indicates whether to generate a video output or not. If provided, the script will generate a video with the tracking results.

  • --vizualize: Another flag that determines whether to visualize the tracking process or not. When included, it enables the visualization of the tracking in real-time.

  • --o_name OUTPUT_NAME: This argument specifies the name of the output video file that will be generated by the script. The user can provide a custom name for the output file.

  • --detections DETECTIONS_FILE: This argument takes the path to the input JSON file containing bounding box detections of objects in the video. The script utilizes this file to perform object tracking.

Additional Parameters:

  • --stop-threshold STOP_THRESHOLD: This parameter sets the threshold to stop detection when an object has been stopped for more than a specified number of times. It helps in filtering out stationary objects.

  • --speed-threshold SPEED_THRESHOLD: This parameter determines the threshold for stopping the detection of speed. Objects moving below this threshold will not be tracked for speed estimation.

  • --thr THRESHOLD: This parameter specifies the minimum Intersection over Union (IoU) required to keep tracking an object. If the IoU falls below this threshold, the object may be considered lost.

  • --max_age MAX_AGE: This parameter sets the maximum number of frames an object can be 'skipped' before it is considered lost by the Kalman Filter.

Task 2

cd Week4/Task2

Run mot

TODO

Put training data inside Task2 folder like this:

Data
└───train
    ├───S01
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    ├───S03
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    └───S04
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python inverse_projection.py

This will save th camera tracklets csv's and the videos for visualization

To compute the IDF1 first concatenate the tracklets files and gt files with:

python concatenate_csv.py

And run trackeval TODO

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➤ Requirements

Install the requirements with the following command:

pip install -r Week4/requirements.txt