Multi-Object tracking using Deep Learning


Multi-Object Tracking in basketball videos using Deep Learning.

Goal: Applying GRU (= Gated Recurrent Unit) in order to track basketball players in videos.

Programming language: Python

Steps that I think that need to be taken:

1. Extract frames from video at timestamps specified the labels

2. Detect objects that need to be tracked using a pre-trained network (e.g., ResNet-50, VGG16 or alike)

3. Match detections with ground_truth bounding box coordinates

4. Apply GRU to track the players in multiple frames and predict the position of the players in frame t + 1.

5. Evaluate the performance using a MOT-2D benchmark

video to be tracked: [login to view URL]

Attachment: bounding box coordinates of each player at a certain time.

Contains the following columns:

- column 1: YouTube ID

- column 2: Time corresponding to the video frame, in microsec.

- column 3: Top-left x-coordinate of player bounding box in frame relative to frame width.

- column 4: Top-left y-coordinate of player bounding box in frame relative to height

- column 5: Width of player bounding box relative to frame width.

- column 6: Height of player bounding box relative to frame height.

- column 7: A player ID

Evner: Machine Learning (ML), Python

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Om arbejdsgiveren:
( 0 bedømmelser ) Breda, Netherlands

Projekt ID: #19256620

11 freelancere byder i gennemsnit €271 på dette job


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