17986234. SELF-SUPERVISED LEARNING FOR VIDEO ANALYTICS simplified abstract (Cisco Technology, Inc.)
Contents
- 1 SELF-SUPERVISED LEARNING FOR VIDEO ANALYTICS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SELF-SUPERVISED LEARNING FOR VIDEO ANALYTICS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
SELF-SUPERVISED LEARNING FOR VIDEO ANALYTICS
Organization Name
Inventor(s)
Hugo Latapie of Long Beach CA (US)
Ozkan Kilic of Long Beach CA (US)
Adam James Lawrence of Pasadena CA (US)
Ramana Rao V. R. Kompella of Cupertino CA (US)
SELF-SUPERVISED LEARNING FOR VIDEO ANALYTICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17986234 titled 'SELF-SUPERVISED LEARNING FOR VIDEO ANALYTICS
Simplified Explanation
The patent application describes a device that analyzes video data to detect events depicted in the video using self-supervised learning techniques. The device represents spatial characteristics of an object over time, associates different portions of the data with behavioral regimes, generates ground truth labels for frames based on changes in behavioral regimes, and trains a model to detect events using the ground truth labels.
- The device analyzes video data to represent spatial characteristics of an object over time.
- It associates different portions of the data with behavioral regimes of the object.
- Ground truth labels for frames are generated based on changes in the behavioral regimes.
- A self-supervised model is trained to detect events in the video data using the ground truth labels.
Potential Applications
This technology could be applied in various fields such as surveillance, sports analysis, wildlife monitoring, and human behavior recognition.
Problems Solved
This technology solves the problem of accurately detecting events in video data by associating behavioral regimes with spatial characteristics of objects over time.
Benefits
The benefits of this technology include improved event detection accuracy, automated labeling of video data, and efficient analysis of object behavior over time.
Potential Commercial Applications
Potential commercial applications of this technology include video surveillance systems, sports analytics software, wildlife tracking devices, and behavior monitoring tools.
Possible Prior Art
One possible prior art for this technology could be existing video analytics systems that use machine learning algorithms to detect events in video data.
Unanswered Questions
How does the device handle complex and dynamic environments in video data analysis?
The device may use advanced algorithms to adapt to changing environments and account for complex interactions between objects in the video data.
What are the limitations of the self-supervised model in detecting events in video data?
The self-supervised model may have limitations in detecting rare or unexpected events that are not well-represented in the training data. Additional research may be needed to address these limitations.
Original Abstract Submitted
In one embodiment, a device represents spatial characteristics of an object depicted in video data over time as one or more timeseries. The device associates different portions of the one or more timeseries with behavioral regimes of the object. The device generates ground truth labels for frames of the video data based on changes in the behavioral regimes of the object associated with those frames. The device trains a self-supervised model to detect an event depicted in the video data using the ground truth labels and their associated frames.
