Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos
Authors: Aniket Bera, Tanmay Randhavane, Dinesh Manocha
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated its accuracy with a user study (88.48%) and evaluated its performance on different videos with tens of pedestrians. One example is the large crowd gathered in Washington, DC for the Presidential Inauguration (January 2017) using PBS HD video footage (see Figure 1). Table 1: Performance of PTC (Personality Trait computation) and GMD (Global Movement Dynamics) algorithms on different crowd videos. We highlight the number of pedestrians used for personality classification, the number of video frames used for extracted trajectories, and the running time (in seconds). |
| Researcher Affiliation | Academia | Aniket Bera, Tanmay Randhavane, and Dinesh Manocha Department of Computer Science, University of North Carolina at Chapel Hill ab@cs.unc.edu |
| Pseudocode | No | The paper contains mathematical formulations and descriptions of the approach, but no explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor any structured, code-like procedures. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions using 'PBS HD video footage' from the 2017 Presidential Inauguration and lists other internal dataset names like 'Manko', 'Marathon', 'Explosion', etc. in Table 1, but it does not provide any concrete access information (links, DOIs, specific repositories, or formal citations) for a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages or sample counts for training, validation, and test sets) for reproducibility. |
| Hardware Specification | Yes | We have implemented our system on a Windows 10 desktop PC with Intel Xeon E5-1620 v3 with 16 GB of memory and we use four cores for PTC and GMD computations. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components or libraries (e.g., 'Python 3.8' or 'TensorFlow 2.0'). |
| Experiment Setup | No | The paper describes the overall methodology and some conceptual parameters (e.g., time window 'w', number of clusters 'K') but does not provide specific numerical values for hyperparameters or detailed system-level training configurations for the model itself. |