Crowd Motion Monitoring with Thermodynamics-Inspired Feature
Authors: Xinfeng Zhang, Su Yang, Yuan Yan Tang, Weishan Zhang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show promising performance compared with the state of the art methods. With UMN data (http://mha.cs.umn.edu/movies/crowdactivity-all.avi), a comparison of our method with Nonextensive Entropy (Susan and Hanmandlu 2013), Sparse Reconstruction Cost method (Yang, Junsong and Ji 2011), Particle Swarm Optimization based Social Force Model (PSO-SFM) (Raghavendra et al. 2011), Social Force Model (Mehran, Oyama and Shah 2009), and Optical Flow methods (Andrade, Blunsden and Fisher 2006) is done in terms of both the averaged AUC (area under ROC curve) and the speed. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Intelligent Information Processing, College of Computer Science, Fudan University, Shanghai 201203, China 2Department of Computer and Information Science, University of Macau, Macau, China 3Department of Software Engineering, China University of Petroleum, Qingdao 266580, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about releasing open-source code or provide links to a code repository for the methodology described. |
| Open Datasets | Yes | With UMN data (http://mha.cs.umn.edu/movies/crowdactivity-all.avi), a comparison of our method with Nonextensive Entropy (Susan and Hanmandlu 2013), Sparse Reconstruction Cost method (Yang, Junsong and Ji 2011), Particle Swarm Optimization based Social Force Model (PSO-SFM) (Raghavendra et al. 2011), Social Force Model (Mehran, Oyama and Shah 2009), and Optical Flow methods (Andrade, Blunsden and Fisher 2006) is done in terms of both the averaged AUC (area under ROC curve) and the speed. Table 2 shows the confusion matrix in identifying normal and abnormal behaviors on UMN and PETS2009 S3 dataset (http://www.cvg.rdg.ac.uk/PETS2009). |
| Dataset Splits | No | The paper mentions using UMN and PETS2009 S3 datasets for evaluation and provides performance metrics like AUC and confusion matrix data, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware components (e.g., CPU, GPU models, memory, cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers that would be needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the decision-making process (thresholding and Gaussian model) but does not provide specific hyperparameters, training configurations, or detailed system-level settings for the experimental setup. |