Depth-Based Object Re-Identification via Frame Quality Weighting
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Solution Overview
Problem
Existing object re-identification methods face challenges in dealing with large variations in illumination, pose, and viewpoint, especially with limited training data, and are susceptible to occlusions and cluttered backgrounds, which affects their accuracy in recognizing objects across different views.
Innovation Solution
An object re-identification method that uses a machine-learning model to assess the quality of video frames and weight confidences based on frame quality, incorporating a temporal attention model and a split-rate color-to-depth transfer scheme to leverage large RGB training datasets for depth-based object re-identification, improving robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object re-identification methods are used, then the system can recognize objects in simple conditions, but accuracy deteriorates when facing large variations in illumination, pose, and viewpoint
Solution Approach 1:
The patent transforms RGB image data into depth map representations, fundamentally changing the data parameter from color/intensity information to spatial distance information. This parameter transformation makes the representation invariant to illumination changes and color variations, directly resolving the contradiction between recognition accuracy and robustness to lighting conditions
Solution Approach 2:
The patent introduces depth maps as an intermediary representation between the original RGB images and the final object recognition decision. This intermediary depth-based representation serves as a bridge that preserves geometric information while eliminating sensitivity to illumination and color variations, enabling accurate re-identification across different viewing conditions
2Measurement precision
If frame predictions are uniformly weighted, then the processing is simple, but accuracy deteriorates when some frames have lower quality due to occlusions or poor lighting
Solution Approach 1:
The patent implements a dynamic weighting mechanism where each frame's contribution to the final re-identification decision is adaptively adjusted based on its quality metrics. Instead of static uniform weighting, the system dynamically evaluates frame quality (considering factors like occlusion level, lighting conditions, and detection confidence) and assigns weights accordingly, thereby improving measurement precision without excessive complexity
Solution Approach 2:
The patent performs preliminary quality assessment of each frame before combining them for final re-identification. By pre-evaluating frame quality metrics and determining weights in advance, the system avoids complex real-time adjustments during the decision-making process, achieving accurate weighted fusion with manageable computational complexity
3Reliability
If depth-based features are used directly without RGB training, then the system is simpler, but performance deteriorates due to limited depth training data
Solution Approach 1:
The patent creates a virtual copy of the abundant RGB training data in the form of synthetic depth maps. By generating depth representations from readily available RGB images during training, the system effectively copies the large-scale RGB dataset into the depth domain, solving the data scarcity problem without requiring actual depth camera training data
Solution Approach 2:
The patent uses RGB images as an intermediary to bootstrap depth-based learning. The abundant RGB data serves as a proxy or surrogate for depth data during the training phase, allowing the model to learn depth-based features indirectly through the RGB-to-depth transformation, thereby overcoming the limitation of limited actual depth training samples
Data Source
AI summary
An object re-identifier. For each of a plurality of frames of a video, a quality of the frame is assessed and a confidence that a previously-recognized object is present in the frame is determined. The determined confidence for the frame is weighted based on the assessed quality of the frame such that frames with higher relative quality are weighted more heavily than frames with lower relative quality. An overall confidence that the previously-recognized object is present in the video is assessed based on the weighted determined confidences.