Video Tracking via Siamese Networks and Bayesian Optimization
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Solution Overview
Problem
Current video tracking technologies face challenges with slow processing speeds due to online training requirements and classification-based approaches that limit generalization and efficiency, lacking integration of deep learning with classical statistics and machine learning for adaptive and principled tracking.
Innovation Solution
A deep Siamese network integrated with a dynamic Bayesian optimization framework for efficient video tracking, using Gaussian Process priors to model temporal coherence and minimize the number of image queries through an acquisition function that balances exploration and exploitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are used for video tracking to improve robustness, then tracking accuracy is improved, but processing speed deteriorates due to online training requirements
Solution Approach 1:
The system performs offline training of the deep learning model before actual video tracking, preparing the model in advance so that no online training is needed during real-time tracking. This preliminary action separates the training phase from the inference phase, allowing robust tracking without real-time training overhead.
Solution Approach 2:
The patent extracts and removes the online training component from the tracking pipeline, keeping only the inference stage. By taking out the training process entirely from real-time operation, the system maintains accuracy while eliminating the speed bottleneck associated with online learning.
2Measurement precision
If classification-based approaches are used for tracking, then object localization is achieved, but the number of image patches and network passes increases, reducing efficiency
Solution Approach 1:
The patent changes the output parameter of the neural network from class probabilities to continuous coordinate predictions. Instead of predicting discrete class labels that require multiple patches and passes, the network directly outputs continuous bounding box coordinates, reducing the number of network evaluations needed while maintaining localization precision.
Solution Approach 2:
The patent replaces the mechanical process of aggregating multiple image patches and performing multiple network passes with a direct regression-based coordinate prediction. This substitution eliminates the iterative patch-based search mechanism in favor of a more efficient direct prediction approach.
3Adaptability or versatility
If generic object trackers are developed to improve generalization, then adaptability is improved, but integration with classical statistics and machine learning approaches remains incomplete
Solution Approach 1:
The patent merges deep learning regression-based tracking with Bayesian optimization and Gaussian process uncertainty estimation into a unified framework. This combination integrates classical statistical methods (Bayesian inference, Gaussian processes) with modern deep learning, creating a more complete and adaptable system that leverages the strengths of both approaches.
Solution Approach 2:
The patent creates a composite tracking system that combines multiple methodological 'materials': deep learning feature extraction, regression-based coordinate prediction, Bayesian optimization for search, and Gaussian process uncertainty modeling. This composite approach achieves superior generalization by integrating diverse technical paradigms into a cohesive workflow.
Data Source
AI summary
An apparatus, method, system and computer readable medium for video tracking. An exemplar crop is selected to be tracked in an initial frame of a video. Bayesian optimization is applied with each subsequent frame of the video by building a surrogate model of an objective function using Gaussian Process Regression (GPR) based on similarity scores of candidate crops collected from a search space in a current frame of the video. A next candidate crop in the search space is determined using an acquisition function. The next candidate crop is compared to the exemplar crop using a Siamese neural network. Comparisons of new candidate crops to the exemplar crop are made using the Siamese neural network until the exemplar crop has been found in the current frame. The new candidate crops are selected based on an updated surrogate model.