Edge Video Analytics Using Latent Space Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video camera systems at the network edge face inefficiencies in bandwidth usage due to the need to upload large amounts of video data, with current methods being computationally intensive and impractical for real-time filtering of informative frames, especially when dealing with dynamic queries and limited CPU resources.
Innovation Solution
A method using embeddings and a latent space to classify video frames in real-time, allowing for the reduction of dimensionality and efficient processing, where video and text features are mapped into a joint space for similarity computation, enabling the filtering of non-informative frames and reducing bandwidth demand by transmitting only relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video frames are uploaded through the network for analysis, then cloud-based processing and storage are achieved, but network bandwidth consumption increases significantly
Solution Approach 1:
The system segments video data transmission by filtering and selecting only informative frames for upload to the cloud, while processing non-informative frames locally. This segmentation reduces network bandwidth consumption while maintaining reliable video analysis capability through selective data transmission.
Solution Approach 2:
The edge device acts as an intermediary between the video camera and cloud server, performing local filtering and preprocessing of video frames. This intermediary function reduces the burden on network bandwidth by eliminating unnecessary data transmission while preserving cloud-based analysis capabilities for relevant frames.
2Loss of information
If traditional video frame filtering methods are used, then informative frames can be identified, but computational complexity and CPU requirements increase
Solution Approach 1:
The system extracts only the essential features from video frames using efficient algorithms suitable for edge devices. By taking out only the critical information needed for frame classification rather than performing full video analysis locally, the system maintains accurate frame selection while reducing computational complexity and CPU requirements.
Solution Approach 2:
The system changes the parameters of video processing by using optimized feature extraction methods and efficient similarity computation algorithms. These parameter changes enable accurate frame filtering with reduced computational overhead, making the system suitable for deployment on resource-constrained edge devices.
3Measurement precision
If annotated training data is used for each object, then detection accuracy improves, but adaptability to new queries decreases
Solution Approach 1:
The system implements a universal embedding space that can represent multiple objects and queries in a unified framework. This universal representation enables the system to maintain high detection accuracy across different object types while being adaptable to new queries without requiring retraining, as the embedding space naturally generalizes to unseen categories.
Solution Approach 2:
The system performs preliminary encoding of queries into the embedding space in advance, allowing for efficient comparison with video frames. This preliminary action enables rapid adaptation to new queries by simply adding new query embeddings to the existing space without retraining, maintaining both accuracy and versatility.
Data Source
AI summary
Training a classifier using embeddings and building a latent space is disclosed. The embeddings may be based on weights in a trained machine learning model. Also, operation of the classifier to process video segments in real-time using the using the weights and the latent space is disclosed. The embeddings and the latent space allow the classification to be performed at an overall reduced dimensionality. The latent space is designed to efficiently scale with an increasing number of queries to permit fast search through the space. Embodiments permit real-time operation on video with dynamic features. The classifier reduces the bandwidth demand of video camera-equipped devices at a network edge by setting aside, accurately, non-informative video sequences rather than uploading video too many things over the network. Applications include security cameras, robots and augmented reality glasses.