Video Object Classification Using Intra-Inter Frame Analysis
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Solution Overview
Problem
Current object classification methods in advanced video surveillance systems lack robustness and flexibility, failing to accurately classify detected objects and track them effectively, leading to inefficient video storage and event definition.
Innovation Solution
A combination of intra-frame and inter-frame classification methods, using features extracted from single images and sequential frames respectively, with a hierarchical classification approach involving Radial Basis Function Neural Nets and Cluster-Based classifiers, to determine object types and dominant colors, enhancing classification accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current object classification methods are used, then basic analytic functions can be performed, but classification accuracy and robustness are insufficient
Solution Approach 1:
The classification process is divided into three distinct stages: intra-frame classification using single-image features, inter-frame classification using temporal information from multiple frames, and sequential classification that integrates both approaches. This segmentation allows each stage to specialize in specific aspects, with intra-frame handling spatial features and inter-frame handling temporal patterns, thereby improving overall accuracy and robustness
Solution Approach 2:
The classification system implements a hierarchical structure where intra-frame classification results serve as input to inter-frame classification, which in turn feeds into sequential classification. This nested arrangement allows lower-level classifications to be refined by higher-level contextual information, enabling the system to leverage both detailed single-frame features and broader temporal patterns for final classification decisions
2Adaptability or versatility
If broadly defined events are stored, then more video storage space is consumed, but specific event detection becomes possible
Solution Approach 1:
The system performs preliminary classification of detected objects into specific categories (e.g., vehicle types, pedestrian classes) before storing video data. By pre-classifying objects using the three-stage classification method, the system can selectively store only the portions of video that contain objects of interest, rather than storing all video data, thereby reducing storage requirements while maintaining flexibility for specific event detection
Data Source
AI summary
A combination of inter-frame and intra-frame and sequential classification methods provide for robust and flexible object classification. Intra-frame classification determines the object type based on features extracted from a single image. Inter-frame classification determines object types based on the features extracted from several sequential frames. A sequential classification combines the results of the inter- and intra-frame classifications over a sequence of images.