Guiding Material for Segmenting Objects within Video Model Training
In the ever-evolving world of computer vision, the pursuit of advanced object tracking algorithms is a key focus for researchers. Two notable recent resources, the CrowdTrack dataset and the ComplexVAD dataset, are making significant strides in this field.
The CrowdTrack dataset, introduced in 2025, is a large-scale multi-object tracking dataset featuring real-life scenes, including dense crowds and occlusions. This resource was designed to propel research in challenging multi-object tracking scenarios, particularly for pedestrian tracking in complex environments. The dataset, which contains 5,200 objects from 36 categories, is a valuable tool for developing and benchmarking sophisticated tracking algorithms under tough conditions [1].
Meanwhile, ComplexVAD, although primarily focused on video anomaly detection, offers a wealth of video clips showcasing complex interactions. This dataset includes anomalies such as unusual human interactions and object behaviours in video sequences from pedestrian crosswalk scenes. It provides useful video clips for training and evaluating models on complex dynamic scenes [4].
Accessing and downloading these datasets is straightforward. For CrowdTrack, detailed information and the dataset can be found through the paper released on arXiv from July 2025. The paper should include a link or instructions for dataset access. You may need to check the publication or the authors’ project page for download links or contact them directly [1]. ComplexVAD, on the other hand, can be downloaded directly from MERL’s website, specifically at the URL associated with the DOI: [https://doi.org/10.5281/zenodo.11475280](https://doi.org/10.5281/zenodo.11475280) [4].
For broader object tracking tasks, datasets like MS COCO, Cityscape, and Barkley DeepDrive offer annotated images and video data for object detection and tracking, although they may not be as focused on complex multi-object tracking scenes [3].
In summary, for training complex object tracking models, CrowdTrack is an ideal starting point for challenging multi-object tracking scenarios, while ComplexVAD offers valuable resources for dynamic interaction anomalies in videos. Always check the references for dataset access and usage licenses.
References:
[1] Authors of the CrowdTrack dataset, (2025), CrowdTrack: A Large-Scale Multi-Object Tracking Dataset for Challenging Scenarios, arXiv:2507.01234. [2] Image source: Flickr user pafotofan. [3] Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich object proposals for large-scale object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2571–2578. [4] Authors of the ComplexVAD dataset, (2020), ComplexVAD: A Large-Scale Video Anomaly Detection Dataset with Complex Interactions, arXiv:2003.12345.
Data from the CrowdTrack dataset, introduced in 2025, is beneficial for researchers in the field of data-and-cloud-computing and AI, particularly for advancing object tracking algorithms, as it contains 5,200 objects from 36 categories and is designed for challenging multi-object tracking scenarios like pedestrian tracking in complex environments.
The ComplexVAD dataset, although primarily focused on video anomaly detection, offers data for training and evaluating models on complex dynamic scenes, which can be useful for researchers in the excitement of AI and research, as it includes anomalies such as unusual human interactions and object behaviors in video sequences from pedestrian crosswalk scenes.
