Welcome to your comprehensive guide on implementing object detection and tracking with the CMU DIVA system! In this walkthrough, we will explore how to efficiently set up the repository, run object detection on surveillance videos, and optimize the code for performance. Let’s get started!

Introduction to the System

The CMU system utilizes advanced algorithms for detecting and tracking objects in surveillance videos. At its core, it leverages Faster RCNN with a Resnet-101 backbone enhanced by dilated CNN and Feature Pyramid Networks (FPN). Imagine this setup as a skilled detective (the Faster RCNN) with a keen eye (Resnet-101) who can spot subtle details (dilated CNN and FPN) in a crowd—a combination that allows for highly effective detection, especially for small objects in outdoor scenes.

Setting Up Your Environment

• Make sure you have the following dependencies installed:
• Tensorflow-GPU==1.15
• Python 2 or 3
• numpy
• scipy
• sklearn
• cv2
• matplotlib
• pycocotools

Running Object Detection and Tracking

Follow these steps to get your detection model running:

1. First, download the necessary test videos and the pre-trained model:

   $ wget https://precognition.teamsharesdiva_obj_detect_models/v1-val_testvideos.tgz
   $ tar -zxvf v1-val_testvideos.tgz
   $ wget https://precognition.teamsharesdiva_obj_detect_models/models/obj_v3_model.tgz
   $ tar -zxvf obj_v3_model.tgz

2. Run the object detection and tracking on the videos:

   $ python obj_detect_tracking.py --model_path obj_v3_model --version 3 --video_dir v1-val_testvideos 
   --video_lst_file v1-val_testvideos.lst --frame_gap 1 --get_tracking --tracking_dir test_track_out

   To view the output in COCO json format:

   --out_dir test_json_out --visualize --vis_path test_vis_out

3. To boost performance, increase the frame gap:

   --frame_gap 8

   The tracking results will be in test_track_out in MOTChallenge format.

Visualizing Tracking Results

To visualize the tracking results, you need to put the Person and Vehicle tracks into the same video. Follow these commands:

$ python get_frames_resize.py v1-val_testvideos.abs.lst v1-val_testvideos_frames --use_2level
$ python tracks_to_json.py test_track_out v1-val_testvideos.abs.lst test_track_out_json
$ python vis_json.py v1-val_testvideos.abs.lst v1-val_testvideos_frames test_track_out_json test_track_out_vis
$ ffmpeg -framerate 30 -i test_track_out_vis/VIRAT_S_000205_05_001092_001124/VIRAT_S_000205_05_001092_001124_F_%08d.jpg vis_video.mp4

Troubleshooting Tips

If you encounter any issues while setting up or running the system, consider the following:

• Check if all necessary libraries are installed and compatible with your Python version.
• Ensure that the correct model paths are specified in the commands.
• If you run into problems with certain video formats, convert those AVI videos to MP4 for better compatibility.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

In conclusion, the CMU Object Detection Tracking system offers potent tools for analyzing video data effectively. Employing a mix of modern techniques, such as multi-camera tracking and ReID capabilities, positions you for high performance in your surveillance applications. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.