Semantic Analysis for Crowded Scenes Based on Non-Parametric Tracklet Clustering
Authors: Allam S. Hassanein, Mohamed E. Hussein, Walid Gomaa
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative and quantitative experimental evaluation on multiple crowded scenes datasets, principally, the challenging New York Grand Central Station video, demonstrate the state of the art performance of our method. |
| Researcher Affiliation | Academia | 1Cyber Physical Systems Lab., Egypt-Japan University of Science and Technology, Alexandria, Egypt 2 Faculty of Engineering, Alexandria University, Alexandria, Egypt {allam.shehata,mohamed.e.hussein,walid.gomaa}@ejust.edu.eg |
| Pseudocode | No | The paper describes the algorithm and process in prose, but it does not include any structured pseudocode blocks, algorithm figures, or sections explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper states 'More detailed results and resources associated with this work can be found online2. 2http://www.cps.ejust.edu.eg/index_files/ijcai_2016.htm'. This statement is ambiguous and does not explicitly confirm that the source code for the described methodology is available. It could refer to data, supplementary materials, or other resources. |
| Open Datasets | Yes | Experiments are conducted on multiple datasets. However, most of our analysis is performed on the challenging New York s Grand Central station video [Zhou et al., 2011], which is a 33-minute video with 540 960 resolution and a frame rate of 25 FPS. |
| Dataset Splits | No | The paper describes the datasets used and how ground truth was obtained for evaluation. However, it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits for their model training or evaluation). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions techniques like 'minimum eigen features method', 'Gaussian Mixture Models', and 'Kanade-Locus-Tomasi (KLT) tracker', but it does not specify any software names with version numbers for these or any other dependencies. |
| Experiment Setup | Yes | In our implementation, to obtain tracklets in a given crowded scene, we first detect interest points using the minimum eigen features method [Shi and Tomasi, 1994] in foreground regions, which are identified using background subtraction via Gaussian Mixture Models [Stauffer and Grimson, 1999], learned from the first five frames. Then, the detected points are tracked using the standard Kanade-Locus-Tomasi (KLT) tracker [Tomasi and Kanade, 1991]. All tracklets are stopped, collected, and tracking is restarted every 25 frames, which makes all our tracklets having the same fixed length. 160 pixels in our implementation. |