Online Detection of Abnormal Events Using Incremental Coding Length
Authors: Jayanta Dutta, Bonny Banerjee
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art. |
| Researcher Affiliation | Academia | Jayanta K. Dutta and Bonny Banerjee Institute for Intelligent Systems, and Department of Electrical & Computer Engineering The University of Memphis Memphis, TN 38152, USA {jkdutta, bbnerjee}@memphis.edu |
| Pseudocode | Yes | Algorithm 1 Online Dictionary Update |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The UCSD dataset (Mahadevan et al. 2010), UMN dataset (Mehran, Oyama, and Shah 2009), and Subway dataset (Adam et al. 2008) are used, all of which are commonly used benchmark datasets with proper citations. |
| Dataset Splits | Yes | UCSD dataset: Ped1 contains 34 training and 36 testing video clips... Ped2 contains 16 training and 12 testing video clips... UMN dataset: ...the first 400 frames of each scene were used to learn the dictionary... The other frames were used for testing. Subway dataset: ...The first ten minutes of each video were used to learn the dictionary... |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions techniques like Orthogonal Matching Pursuit (OMP) and Batch-OMP but does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | No | The paper mentions input cuboid sizes (13x13x10 pixels) and frame resolution resizing (320x240 pixels) but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs, optimizer settings) or other detailed system-level training configurations. |