# Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking

arXiv: https://arxiv.org/abs/1607.06408 · HTML: https://mlsanity.com/paper/1607.06408/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 6 of 6)

- [Open surgery tool classification and hand utilization using a multi-camera system](https://mlsanity.com/paper/2111.06098/) (2021)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [ReL-SAR: Representation Learning for Skeleton Action Recognition with Convolutional Transformers and BYOL](https://mlsanity.com/paper/2409.05749/) (2024)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras](https://mlsanity.com/paper/2511.16521/) (2025)
- [FedCVU: Federated Learning for Cross-View Video Understanding](https://mlsanity.com/paper/2603.21647/) (2026)

## Abstract (arXiv)

Action recognition has received increasing attention from the computer vision and machine learning communities in the last decade. To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet. Apart from the "in-the-wild" datasets, the training and test split of conventional datasets often possess similar environments conditions, which leads to close to perfect performance on constrained datasets. In this paper, we introduce a new dataset, namely Multi-Camera Action Dataset (MCAD), which is designed to evaluate the open view classification problem under the surveillance environment. In total, MCAD contains 14,298 action samples from 18 action categories, which are performed by 20 subjects and independently recorded with 5 cameras. Inspired by the well received evaluation approach on the LFW dataset, we designed a standard evaluation protocol and benchmarked MCAD under several scenarios. The benchmark shows that while an average of 85% accuracy is achieved under the closed-view scenario, the performance suffers from a significant drop under the cross-view scenario. In the worst case scenario, the performance of 10-fold cross validation drops from 87.0% to 47.4%.