Multi-object tracking with learnable cost functions
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Solution Overview
Problem
Existing multi-object tracking systems rely on hand-crafted cost functions and are limited to linear formulations, which are inefficient and require tedious manual design, failing to effectively associate object detections over time.
Innovation Solution
A surveillance system that uses a learning model to jointly learn arbitrarily parameterized and differentiable cost functions for all variables in a linear program, employing bi-level optimization to minimize loss and associate object detections with bounding boxes, enabling end-to-end learning of cost functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-crafted cost functions are used in multi-object tracking, then the system can be implemented with traditional methods, but the tracking performance is limited and manual design is tedious
Solution Approach 1:
The patent replaces hand-crafted cost function design (mechanical/manual system) with deep learning-based automatic cost function learning (intelligent system). The neural network automatically learns optimal cost functions from data, eliminating the need for manual design and tuning while improving tracking performance.
Solution Approach 2:
The patent transforms fixed hand-crafted cost function parameters into learnable parameters through deep neural networks. The cost function parameters are no longer static but are dynamically optimized through gradient descent and backpropagation, allowing the system to adapt to different tracking scenarios.
2Adaptability or versatility
If linear cost functions are used for association, then the formulation remains simple, but the system cannot effectively learn complex association patterns
Solution Approach 1:
The patent makes the cost function dynamic by using neural networks that can adapt their parameters based on input data. Instead of static linear cost functions, the system uses learnable neural network models that dynamically adjust their behavior to capture complex association patterns in the data.
Solution Approach 2:
The patent replaces simple linear cost function formulations with sophisticated neural network-based cost functions. This substitution enables the system to learn complex non-linear association patterns while maintaining a unified formulation that can be optimized end-to-end.
3Reliability
If cost functions are learned only for parts of the variables, then the learning process is simpler, but the overall tracking accuracy is reduced
Solution Approach 1:
The patent merges the learning of cost functions for all variables into a unified end-to-end training process. Instead of separately learning cost functions for different parts of the system, the neural network learns all cost function parameters simultaneously through joint optimization, improving overall tracking accuracy.
Solution Approach 2:
The patent creates a universal learning framework that handles all cost function parameters through a single neural network model. This multi-functional approach allows the same learning mechanism to optimize cost functions for detection, association, and tracking variables simultaneously.
Data Source
AI summary
A surveillance system and method are provided. The surveillance system includes at least one camera configured to capture a set of images of a given target area that includes a set of objects to be tracked. The surveillance system includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The surveillance system includes a processor configured to perform surveillance of the target area to (i) detect the objects and track locations of the objects by applying the learning model to the images in a surveillance task that uses the multi-object tracking, and (ii), provide a listing of the objects and their locations for surveillance task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.