Object Tracking With Kalman Prediction and Dynamic Image Cropping
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Solution Overview
Problem
Current object detection and tracking systems face challenges in accurately predicting the future location of moving objects, especially in dynamic environments, and efficiently adjusting the field of view to maintain object tracking, particularly when objects deviate significantly from the image center.
Innovation Solution
The system employs a neural network for object detection and a Kalman filter for predicting future object locations, generating control signals to adjust the camera's field of view through zoom and gimbal controls, and cropping images to focus processing resources on the object's predicted path, ensuring efficient tracking and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the camera field of view is adjusted to track objects that deviate significantly from the image center, then tracking accuracy is improved, but device complexity and response time increase
Solution Approach 1:
The Kalman filter predicts future object locations in advance before the object actually moves to those positions. This preliminary prediction allows the system to proactively adjust the camera field of view and crop regions, rather than reactively responding after the object has already deviated from the current view, thereby improving tracking accuracy while managing system complexity
Solution Approach 2:
The Kalman filter acts as an intermediary between the object detection system and the camera control system. It processes detection data and generates predicted location information that mediates the coordination between detection and field of view adjustment, simplifying the overall system architecture while enhancing tracking performance
2Productivity
If image cropping is used to focus on predicted object location, then processing efficiency is improved, but information loss increases
Solution Approach 1:
Instead of processing the entire image uniformly, the system applies different processing strategies to different regions. The crop region is dynamically adjusted based on predicted object location, applying high-processing quality to the relevant local area while reducing or eliminating processing in irrelevant areas, thus improving efficiency while minimizing information loss
Solution Approach 2:
The crop region is not fixed but dynamically adjusted based on the predicted future location of the object. This dynamic adaptation allows the system to maintain optimal processing efficiency while preserving necessary image information as the object moves through different positions in the scene
3Measurement precision
If neural network detection is applied to the entire image sequence, then detection accuracy is improved, but computational cost and time increase
Solution Approach 1:
The system extracts only the relevant crop region based on predicted object location and applies the computationally intensive neural network detection only to this smaller region rather than the entire image. This extraction approach maintains detection accuracy for the target object while significantly reducing processing time and computational cost
Data Source
AI summary
An apparatus, method and computer program is described comprising detecting a first object in a first image of a sequence of images using a neural network, wherein the means for detecting the first object provides an object area indicative of a first location of the first object; and tracking the first object, wherein the means for tracking the first object further comprises generating a predicted future location of the first object and generating an updated location of the first object using the neural network. The means for generating the predicted future location of the first object may, for example, receive said object area indicative of a first location of the first object and may receive said updated location information of the first object.