# A comprehensive study of sparse codes on abnormality detection

arXiv: https://arxiv.org/abs/1603.04026 · HTML: https://mlsanity.com/paper/1603.04026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Generalized One-Class Learning Using Pairs of Complementary Classifiers](https://mlsanity.com/paper/2106.13272/) (2021)

## Abstract (arXiv)

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We comprehensively study two types of sparse codes solutions - greedy algorithms and convex L1-norm solutions - and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. Experiments show that combining OMP codes with maximum coordinate detection could achieve state-of-the-art performance on the UCSD dataset [14].