Parts-Based Object Tracking for Vehicle Detection
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Solution Overview
Problem
Current vehicle object detection and tracking systems face challenges in accurately detecting and tracking objects from a moving vehicle, particularly in scenarios requiring real-time collision avoidance and autonomous driving, where precise detection of moving and stationary objects is crucial for safety and efficiency.
Innovation Solution
A method and apparatus that utilize a video camera to receive sequential video frames, segment images into identifiable parts, and employ a particle filter framework with sliding windows and deep learning networks to generate confidence maps and tracking scores, allowing for robust object detection and tracking across varying scales and occlusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection and tracking systems use traditional methods to detect objects from a moving vehicle, then the system complexity is reduced, but the detection precision and tracking accuracy deteriorate
Solution Approach 1:
The patent segments the target object into multiple parts (e.g., head, body, wheels of a vehicle) and tracks each part separately using individual detectors. This segmentation approach improves detection precision by focusing on distinctive features while the modular detector design manages system complexity through organized processing of multiple components
Solution Approach 2:
The patent introduces a part-based dimensional approach by dividing objects into multiple spatial components rather than treating them as single units. This adds a hierarchical dimension to detection (whole object → parts → individual features), improving precision through multi-level analysis while maintaining manageable complexity through structured processing
2Speed
If the system processes video frames in real-time for collision avoidance, then the response speed is improved, but the detection precision may deteriorate due to processing constraints
Solution Approach 1:
The system performs preliminary actions by pre-training detectors for each object part offline, creating ready-to-use detection models. During real-time video processing, these pre-trained detectors are applied directly to new frames without requiring online training, thus maintaining high response speed while preserving detection precision through previously learned features
Solution Approach 2:
The patent implements dynamic tracking by updating part positions and adjusting detector parameters frame-by-frame based on motion patterns. The system adapts to moving objects by continuously refining part locations and bounding boxes in real-time, maintaining both speed through efficient updates and precision through adaptive parameter adjustment
3Adaptability or versatility
If the system tracks multiple objects with varying scales and occlusions, then the adaptability is improved, but the computational complexity increases
Solution Approach 1:
By segmenting objects into parts, the system handles occlusions more effectively - when part of an object is occluded, other parts remain detectable. This segmentation approach improves adaptability to occlusion and scale variations while managing computational complexity through focused detection on individual parts rather than requiring complete object visibility
Solution Approach 2:
The system adapts to varying scales by adjusting detector parameters (such as window sizes and search regions) dynamically based on object distance and size. This parameter adaptation improves versatility across different scales and scenarios while controlling computational complexity through selective parameter adjustment rather than processing all possible scale variations equally
Data Source
AI summary
The method and apparatus segment parts from a main target image, represent each part as a vertex in a spanning tree, use a detector to generate a confidence map of a location of each part in a succeeding video frame and apply scale change to detector sliding windows centered about each pixel in the part location image. In the succeeding video frame, the target location is sampled and a tracking probability is generated for each part bounding box, with the tracking probability having the maximum value being selected as the location of the target in the succeeding video frame.