Videoclassification
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Deepstack_exdark | 53 | 3 years ago | mit | Python | ||||||
| A DeepStack custom model for detecting common objects in dark/night images and videos. | ||||||||||
| Social_distancing_with_ai | 46 | 3 years ago | mit | Jupyter Notebook | ||||||
| Monitor people violating Social Distancing or not wearing Face Masks in public through CCTV footage. | ||||||||||
| Live Cctv | 24 | 2 years ago | mit | Python | ||||||
| To detect any reasonable change in a live cctv to avoid large storage of data. Once, we notice a change, our goal would be track that object or person causing it. We would be using Computer vision concepts. Our major focus will be on Deep Learning and will try to add as many features in the process. | ||||||||||
| Weapon Detection And Classification | 14 | 5 years ago | 3 | mit | Python | |||||
| Weapon Detection & Classification through CCTV surveillance using Deep Learning-CNNs. | ||||||||||
| Person_detection_from_cctv_video | 11 | 4 years ago | Python | |||||||
| detect the no of people every second entering building gate. #person-detection | ||||||||||
| Videoclassification | 10 | 4 years ago | 3 | Jupyter Notebook | ||||||
| Crime detection in cctv footage using deep learning | ||||||||||
| Cognitrack | 9 | 4 years ago | 3 | mit | Python | |||||
| CogniTrack is an Artificial Intelligence powered person tracking system that acquires images from CCTV cameras and tracks individuals appearing in the frame in real-time. | ||||||||||
| Early Detection Of Collective Or Individual Theft Attempts Us Ing Long Term Recurrent Convolutional | 5 | a year ago | 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. | ||||||||||
Alternatives To VideoclassificationSelect To Compare
Alternative Project Comparisons
Popular Cctv Projects
Popular Deep Learning Projects
Popular Media Categories
Related Searches
Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Jupyter Notebook
Deep Learning
Pytorch
Recognition
Action Recognition
Cctv