Context-Adapted Object Detection in Video Streams
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current object detection systems, particularly those using Convolutional Neural Networks (CNNs), lack flexibility and the ability to adapt on-the-fly to the specific characteristics of a video stream, leading to missed objects and false positives due to differences in statistics between training datasets and the actual video content.
Innovation Solution
A context-based object classifying model is applied to object location representations from previous frames of a video stream to generate context-adapted classification probabilities, which are combined with detection probabilities to improve object detection accuracy by introducing bias based on local characteristics of the video stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CNN-based object detection is used to achieve high detection accuracy, then detection precision is improved, but the system lacks adaptability to specific video stream characteristics
Solution Approach 1:
The system dynamically adapts the detection threshold based on video stream characteristics. Instead of using a fixed threshold, the method analyzes the distribution of detection probabilities in the current video stream and adjusts the threshold adaptively, allowing the system to respond to changing conditions while maintaining high detection accuracy.
Solution Approach 2:
The invention changes the parameter (detection threshold) based on the statistical properties of the video stream. By analyzing the detection probability distribution and adjusting the threshold parameter accordingly, the system achieves adaptability to different video streams while preserving the high accuracy of CNN-based detection.
2Ease of operation
If a fixed detection threshold is used to simplify the detection process, then ease of operation is improved, but detection accuracy deteriorates due to statistical differences between training data and video stream
Solution Approach 1:
The system performs self-adjustment by automatically analyzing the detection probability distribution of the current video stream and setting the threshold accordingly. This self-service mechanism eliminates the need for manual threshold tuning while maintaining high detection accuracy, as the system adapts to each video stream's characteristics automatically.
Solution Approach 2:
The method incorporates feedback by using the detected objects and their probabilities from the video stream to adjust the detection threshold. The system continuously monitors the detection results and adapts the threshold based on the observed statistical properties, creating a closed-loop system that improves accuracy without increasing operational complexity.
3Measurement precision
If CNN models with tens of millions of parameters are deployed to achieve high detection accuracy, then detection precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The invention extracts and utilizes only the detection probability outputs from the CNN model, rather than deploying the entire complex model with tens of millions of parameters. By taking out just the essential component (probability scores) and applying a simple statistical thresholding method, the system achieves high detection accuracy with minimal computational overhead and device complexity.
Data Source
AI summary
A context-based object classifying model is applied to a set of object location representations (12, 14), derived from an object detection applied to a frame (10) of a video stream, to obtain a context-adapted classification probability for each object location representation (12, 14). Each object location representation (12, 14) defines a region of the frame (10) and each context-adapted classification probability represents a likelihood that the region comprises an object (11, 13). The model is generated based on object location representations from previous frames of the video stream. It is determined whether the region defined by the object location representation (12, 14) comprises an object (11, 13) based on the context-adapted classification probability and a detection probability. The detection probability is derived from the object detection and represents a likelihood that the region defined by the object location representation (12, 14) comprises an object (11, 13).