Person Detection by Low-rank Sparse Aggregate Channel Features
Pages 58 - 62
Abstract
Human detection in the video has several applications in security and surveillance. Human detection using video is desired to be robust against illumination, occlusions, scale, translation and view angle variations. In this paper, we develop an approach which can improve the performance of the aggregate channel feature for a high view angle. The foreground is estimated using a frame differences approach to identify the location of moving objects in the static camera scene. The sparse basis is included in the aggregate channel feature vector to describe the foreground region of each frame of the video. This approach provides better miss rate versus false positive per image as compared to the existing aggregate channel feature and histogram of the oriented gradient.
References
[1]
Stauffer, C., and Grimson, W. E. L., Adaptive background mixture models for real-time tracking., IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. pp. 246--252, 1999.
[2]
Wang, P., Collins, L., Morton, K., and Torrione, P. Viewpoint adaptation for person detection.,In European Conference on Computer Vision, 2017.
[3]
Dalal, N., and Triggs, B., Histograms of oriented gradients for human detection., In Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886--893, 2005.
[4]
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D., Object detection with discriminatively trained part-based models., IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 9, pp. 1627--1645., 2010.
[5]
Luna, C. A., Losada-Gutierrez, C., Fuentes-Jimenez, D., Fernandez-Rincon, A., Mazo, M., and Macias-Guarasa, J., Robust people detection using depth information from an overhead time of flight camera., Expert Systems with Applications, pp. 240--256, 2017.
[6]
Zhong, W., Lu, H., and Yang, M. H., Robust object tracking via the sparsity-based collaborative model.,Computer vision and pattern recognition (CVPR), pp. 1838--1845, 2012.
[7]
Kalal, Z., Mikolajczyk, K., and Matas, J., Tracking-learning detection., IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 7, pp. 1409--1422, 2012.
[8]
Gaszczak, A., Breckon, T. P., and Han, J. Real-time people, and vehicle detection from Unmanned Ariel Vehicle (UAV) imagery., Intelligent Robots and Computer Vision, 2011.
[9]
Rudol, P., and Doherty, P., Human body detection and geolocalization for Unmanned Ariel Vehicle (UAV) search and rescue missions using color and thermal imagery., In Aerospace Conference, 2008.
[10]
Reilly, V., Solmaz, B., and Shah, M., Geometric constraints for human detection in aerial imagery., Computer Vision, pp. 252--265, 2010.
[11]
Dollar P., Appel, R., and Kienzle, W., Crosstalk cascades for framerate pedestrian detection., Computer Vision 2012. Springer, Berlin, pp. 645--659, 2012.
[12]
Dollr P., Belongie, S. J., and Perona, P., The fastest pedestrian detector in the West., vol. 2, no. 3, pp. 7, 2010.
[13]
Dollr, P., Wojek, C., Schiele, B., and Perona, P., Pedestrian detection: A benchmark, Computer Vision and Pattern Recognition (CVPR), pp. 304--311, 2009.
Index Terms
- Person Detection by Low-rank Sparse Aggregate Channel Features
Recommendations
Background subtraction using hybrid feature coding in the bag-of-features framework
Although numerous algorithms have been proposed for background subtraction with demonstrated success, it remains a challenging problem. One of the main reasons is the lack of effective background model to account for the complex variations of ...
Latent Fingerprint Matching
Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and ...
Real-time video surveillance based on combining foreground extraction and human detection
MMM'08: Proceedings of the 14th international conference on Advances in multimedia modelingIn this paper, we present an adaptive foreground object extraction algorithm for real-time video surveillance, in conjunction with a human detection technique applied in the extracted foreground regions by using AdaBoost learning algorithm and ...
Comments
Information & Contributors
Information
Published In
April 2019
76 pages
ISBN:9781450362474
DOI:10.1145/3330180
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- UPM: Universiti Putra Malaysia
- NITech: Nagoya Institute of Technology
- Iv. Javakhishvili Tbilisi State University, Georgia
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 April 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
ICCBN 2019
ICCBN 2019: 2019 7th International Conference on Communications and Broadband Networking
April 12 - 15, 2019
Nagoya, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in