| jinwchoi/awesome-action-recognition |
3,494 |
|
0 |
0 |
almost 3 years ago |
0 |
|
1 |
|
|
| A curated list of action recognition and related area resources |
| dukebw/lintel |
226 |
|
0 |
0 |
about 7 years ago |
1 |
January 02, 2019 |
16 |
apache-2.0 |
C |
| A Python module to decode video frames directly, using the FFmpeg C API. |
| rohitgirdhar/ActionVLAD |
201 |
|
0 |
0 |
about 7 years ago |
0 |
|
4 |
other |
Python |
| ActionVLAD for video action classification (CVPR 2017) |
| ParitoshParmar/MTL-AQA |
38 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
|
Python |
| What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment |
| karolzak/conv3d-video-action-recognition |
31 |
|
0 |
0 |
almost 5 years ago |
0 |
|
2 |
mit |
Python |
| My experimentation around action recognition in videos. Contains Keras implementation for C3D network based on original paper "Learning Spatiotemporal Features with 3D Convolutional Networks", Tran et al. and it includes video processing pipelines coded using mPyPl package. Model is being benchmarked on popular UCF101 dataset and achieves results similar to those reported by authors |
| lRomul/ball-action-spotting |
31 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
mit |
Python |
| SoccerNet | 1st place solution for Ball Action Spotting Challenge 2023 |
| chow-vincent/tennis_action_recognition |
16 |
|
0 |
0 |
almost 5 years ago |
0 |
|
3 |
mit |
Jupyter Notebook |
| Using deep learning to perform action recognition in the sport of tennis. |
| MichiganNLP/vlog_action_recognition |
15 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
mit |
Python |
| Identifying Visible Actions in Lifestyle Vlogs |
| fubel/stmodeling |
13 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
other |
Python |
| Code for the paper "Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos" |
| kaledhoshme123/Early-Detection-of-Collective-or-Individual-Theft-Attempts-Us-ing-Long-term-Recurrent-Convolutional- |
5 |
|
0 |
0 |
almost 3 years ago |
0 |
|
1 |
mit |
|
| I designed an intelligent system capable of analyzing movement within the videos and detecting suspicious movement that precedes the occurrence of shoplifting crimes. The proposed system can analyze the movement into two primary classifications: the natural movement, and the suspicious movement (with the percentage of each of them being determined.” Thus, the system appears, depending on the percentage of the type of movement, whether the possibility of theft is high or low, or the Confusion movement, which are branched cases depending on the percentage percent accuracy of smart model classification"). The system is integrated with surveillance camera systems that are placed in stores, and the system can at that time alert security personnel in cases where the movement of people in the monitored area appears to be suspicious. The system can also help in cases where it is required to search within a large number of video clips recorded by the surveillance cameras to determine the time moments before the theft crimes. The compressed file contains several video clips on which the system has been tested (the system is waiting for 160 frames to pass, “that is, approximately 3 seconds on average, depending on the frequency of the frames within the video clips or the live broadcast”). I sent you a detailed study of how the system works, and if you like the system and find that it can complement your software systems, I will send you the code and the smart trained model. |
