Pose-Part Re-Identification Models for Large Pose Variations
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Solution Overview
Problem
Traditional re-identification (Re-ID) models struggle with large pose variations and clutter backgrounds, leading to inaccurate object re-identification due to non-rigid pose changes and insufficient consideration of local part information, especially in multi-camera systems.
Innovation Solution
A pose part based model (PPbM) that integrates global and local representations using a deep neural network framework, leveraging accurate keypoint pose estimation to seamlessly handle large pose variations and reduce clutter background impact, trained in an end-to-end manner for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional holistic appearance based re-ID models are used, then the system is simple to implement, but it cannot capture large pose variations of objects due to clutter background
Solution Approach 1:
The patent divides the object into multiple pose parts (head, body, limbs) based on skeleton keypoints. Each pose part is processed separately to extract local features, which are then aggregated to form the global object representation. This segmentation allows the model to handle large pose variations by focusing on invariant local parts rather than the entire object appearance.
Solution Approach 2:
The patent applies different processing strategies to different pose parts based on their local characteristics. The local head uses concatenation-based aggregation while the soft-attention based local head uses attention mechanisms to weight different parts. This local quality approach enables the model to capture pose-specific features while maintaining overall object identity.
2Measurement precision
If aligned re-ID methods based on local grid modeling are used, then each grid has uniform processing, but large pose variations introduce clutter background that reduces accuracy
Solution Approach 1:
The patent extracts skeleton keypoints and pose parts from the object, separating the structural information from the cluttered background. By focusing on the extracted pose parts rather than the entire bounding box, the model eliminates background interference and captures only the relevant object features for re-identification.
Solution Approach 2:
The patent dynamically adjusts the processing of each pose part based on its detected position and orientation. The model adapts to large pose variations by repositioning and reorienting the local grids to match the actual pose of each body part, rather than using fixed uniform grids. This dynamic adaptation maintains measurement precision despite pose changes.
3Ease of manufacture
If classification loss is used for re-ID network training, then the network structure is straightforward, but it does not separate the margin between positive pairs and negative pairs effectively
Solution Approach 1:
The patent changes the loss function parameter from classification loss to triplet loss, which fundamentally alters the training objective. Triplet loss explicitly computes margins between positive pairs (same identity) and negative pairs (different identities), forcing the network to learn discriminative features that separate different object instances while maintaining intra-class similarity.
4Measurement precision
If part-based models are used to model local deformable object structures, then fine-grain recognition is improved, but the structure learning becomes complicated and difficult to integrate into deep neural networks
Solution Approach 1:
The patent introduces skeleton keypoints as an intermediary that bridges the gap between simple deep neural networks and complex part-based models. The skeleton keypoints provide a structured representation of pose parts that can be easily integrated into standard CNN architectures, enabling fine-grain recognition without requiring complicated structure learning algorithms.
Data Source
AI summary
An example apparatus for re-identifying objects includes an image receiver to receive a first image and a second image of an object with an identity. The apparatus also includes a fused model generator to fuse a global representation of the object with local representations of pose parts of the object to generate a fused representation of the object based on the first image. The apparatus further includes an object re-identifier to re-identify the object with the identity in the second image based on the fused representation.