# Long Term Object Detection and Tracking in Collaborative Learning Environments

arXiv: https://arxiv.org/abs/2106.07556 · HTML: https://mlsanity.com/paper/2106.07556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [Learning to Track at 100 FPS with Deep Regression Networks](https://mlsanity.com/paper/1604.01802/) (2016)
- [Discriminative Correlation Filter with Channel and Spatial Reliability](https://mlsanity.com/paper/1611.08461/) (2016)
- [Adaptive Encoding for Constrained Video Delivery in HEVC, VP9, AV1 and VVC Compression Standards and Adaptation to Video Content](https://mlsanity.com/paper/2104.12770/) (2021)

## Built on by (most cited first, 2 of 2)

- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Multimodality of AI for Education: Towards Artificial General Intelligence](https://mlsanity.com/paper/2312.06037/) (2023)

## Abstract (arXiv)

Human activity recognition in videos is a challenging problem that has drawn a lot of interest, particularly when the goal requires the analysis of a large video database. AOLME project provides a collaborative learning environment for middle school students to explore mathematics, computer science, and engineering by processing digital images and videos. As part of this project, around 2200 hours of video data was collected for analysis. Because of the size of the dataset, it is hard to analyze all the videos of the dataset manually. Thus, there is a huge need for reliable computer-based methods that can detect activities of interest. My thesis is focused on the development of accurate methods for detecting and tracking objects in long videos. All the models are validated on videos from 7 different sessions, ranging from 45 minutes to 90 minutes. The keyboard detector achieved a very high average precision (AP) of 92% at 0.5 intersection over union (IoU). Furthermore, a combined system of the detector with a fast tracker KCF (159fps) was developed so that the algorithm runs significantly faster without sacrificing accuracy. For a video of 23 minutes having resolution 858X480 @ 30 fps, the detection alone runs at 4.7Xthe real-time, and the combined algorithm runs at 21Xthe real-time for an average IoU of 0.84 and 0.82, respectively. The hand detector achieved average precision (AP) of 72% at 0.5 IoU. The detection results were improved to 81% using optimal data augmentation parameters. The hand detector runs at 4.7Xthe real-time with AP of 81% at 0.5 IoU. The hand detection method was integrated with projections and clustering for accurate proposal generation. This approach reduced the number of false-positive hand detections by 80%. The overall hand detection system runs at 4Xthe real-time, capturing all the activity regions of the current collaborative group.