Object Re-entry Detection Using Motion Sensor Prediction
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Solution Overview
Problem
Existing systems for egocentric video capture devices face computational intensity and challenges in tracking objects as they move in and out of the camera's field of view, particularly when the user moves, leading to inefficiencies in processing and detection.
Innovation Solution
An image tracking system comprising an image sensing device, processor, and memory with programming instructions to detect and track objects of interest by extracting characteristics, using machine learning, and integrating positional parameters from motion sensors to determine when an object leaves and re-enters the field of view, allowing for efficient monitoring and re-identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes each frame of video to identify the object, then the object detection accuracy is improved, but the computational processing time increases
Solution Approach 1:
The system performs preliminary actions by using motion sensor data to predict the field of view and determine likely object locations before actually processing image frames. This allows the system to prepare detection parameters and focus computational resources only on relevant regions, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
Instead of processing every frame completely, the system applies partial processing by combining motion-predicted regions with selective frame analysis. The system performs excessive action in predicting potential locations using motion sensors, then reduces actual image processing to only those predicted regions, achieving a balance between accuracy and efficiency.
2Reliability
If the system continuously tracks the object in all frames, then the tracking reliability is improved, but the energy consumption increases
Solution Approach 1:
The system implements periodic action by pausing the tracking application when the object is determined to be outside the field of view and resuming when it re-enters. This periodic on-demand tracking maintains reliability by ensuring the object is detected when present, while significantly reducing energy consumption by avoiding continuous processing during periods when the object is not visible.
Solution Approach 2:
The tracking system serves itself by using motion sensor data to automatically determine when tracking should be active or paused. The system monitors its own operational conditions through motion parameters and autonomously adjusts its processing state, eliminating the need for continuous external control while maintaining tracking reliability.
3Measurement precision
If the system processes every frame to detect object re-entry, then the detection accuracy is improved, but the processing efficiency decreases
Solution Approach 1:
The system performs preliminary determination of object re-entry by analyzing motion sensor data and predicted field of view parameters before actually processing image frames for confirmation. This preliminary action identifies probable re-entry events, allowing the system to focus full processing power only on confirmed re-entry moments, thereby maintaining high detection accuracy while improving overall processing efficiency.
Solution Approach 2:
Motion sensor data serves as an intermediary between the object's actual position and the image processing system. The motion sensors provide predictive information about object location and field of view, acting as a mediator that guides the image processing to focus only on relevant frames and regions, thus improving efficiency without sacrificing detection accuracy.
Data Source
AI summary
A mobile electronic device processes a sequence of images to identify and re-identify an object of interest in the sequence. An image sensor of the device, receives a sequence of images. The device detects an object in a first image as well as positional parameters of the device that correspond to the object in the first image. The device determines a range of positional parameters within which the object may appear in a field of view of the device. When the device detects that the object of interest exited the field of view it subsequently uses motion sensor data to determine that the object of interest has likely re-entered the field of view, it will analyze the current frame to confirm that the object of interest has re-entered the field of view.