Annealed Dynamic Histograms for Robust Velocity Tracking
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Solution Overview
Problem
Real-time object tracking in complex environments, such as autonomous vehicles, faces challenges due to occlusions, viewpoint changes, and non-uniform object appearances, leading to noisy velocity estimates that hinder accurate tracking and decision-making.
Innovation Solution
The system employs a Dynamic Bayesian Network combining 3D shape, motion, and color information, using annealed dynamic histograms to refine velocity estimates by subdividing the state space and incorporating a motion model that accounts for occlusions and viewpoint changes, ensuring robust velocity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object tracking methods are used in real-time systems, then the system can operate at high speeds, but the velocity estimates become noisy and inaccurate due to occlusions and viewpoint changes
Solution Approach 1:
The patent segments the state space into multiple discrete cells, allowing the system to track objects through occlusions by maintaining probability distributions across segmented spatial regions. This segmentation enables the tracker to handle partial observations and viewpoint changes by distributing tracking information across multiple cells rather than relying on a single continuous representation.
Solution Approach 2:
The patent implements dynamic histograms that are updated at each time step based on observed object locations and a motion model. The histogram weights are dynamically adjusted to reflect the probability of object presence in each cell, allowing the system to adapt to changing conditions such as occlusions and viewpoint changes while maintaining accurate velocity estimates.
2Measurement precision
If the state space is finely divided into many cells to improve tracking accuracy, then velocity estimation precision improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by updating only the necessary portions of the histogram at each time step based on observed object locations. Rather than processing the entire state space uniformly, the system focuses computational effort on cells with non-zero probability weights, achieving accurate tracking with reduced computational complexity.
Solution Approach 2:
The patent changes the parameter representation from continuous spatial coordinates to discrete histogram cell indices. This parameter transformation enables efficient computation by allowing the system to work with a finite set of discrete states rather than continuous space, reducing computational complexity while maintaining tracking precision through sufficient cell granularity.
Data Source
AI summary
Velocity controllers in accordance with embodiments of the invention enable velocity estimation for tracked objects. One embodiment includes a tracker controller, including: a processor; and a memory containing: a velocity tracker application; a state space describing relationships between measured locations, calculated locations, and changes in locations, where the calculated locations in the state space correspond to unoccluded points on the surface of the tracked object; wherein the processor is configured by the velocity tracker application to: pre-process the state space to identify a tracked object; estimate a velocity of the tracked object using a location history calculated from the measured locations of the tracked object within the state space and a motion model calculated from the state space; and return the velocity of the tracked object.