Video Object Tracking and Classification via Deep Learning Segmentation
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Solution Overview
Problem
Existing visual detection systems require significant computational resources and human intervention for classifying objects in videos, as they often process entire images and are unable to efficiently segment and classify discrete objects in real-time.
Innovation Solution
A method and system that utilize a tracking component to detect and track objects of interest in videos, isolating segments for classification using deep learning, thereby reducing computational requirements and enabling real-time object classification without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes entire images to classify objects, then classification accuracy is maintained, but computational time and resources increase significantly
Solution Approach 1:
The patent divides the image processing task into two stages: first, a tracking component identifies and isolates segments containing objects of interest; second, the classification component processes only these isolated segments rather than entire images. This segmentation approach maintains classification accuracy while dramatically reducing computational time and resources by limiting deep learning processing to only relevant regions.
Solution Approach 2:
The patent extracts and isolates specific segments containing objects of interest from the full image using tracking capabilities. By taking out only the necessary portions (segments with objects) for classification processing, the system avoids the computational burden of processing entire images while preserving the ability to accurately classify objects.
2Measurement precision
If the system uses deep learning to classify objects, then classification accuracy improves, but computational resources and device complexity increase
Solution Approach 1:
The system segments the image into regions containing objects of interest before applying deep learning classification. This reduces the input size and complexity for the deep learning model, allowing high accuracy classification with reduced computational resources compared to processing entire images through the complex deep learning system.
Solution Approach 2:
The tracking component performs preliminary action by identifying, detecting, and isolating segments containing objects of interest before the classification component applies deep learning. This preliminary processing reduces the complexity of the subsequent deep learning task, enabling accurate classification with fewer computational resources.
3Adaptability or versatility
If the system iteratively searches entire images in smaller sub-sections to classify discrete objects, then discrete object classification is achieved, but computational time and resources increase significantly
Solution Approach 1:
The tracking component performs preliminary detection and isolation of discrete objects before classification. By pre-identifying and segmenting regions containing discrete objects of interest, the system avoids the need for iterative searching during classification, significantly reducing computational time while maintaining the ability to classify multiple discrete objects.
Solution Approach 2:
The system segments the image into discrete regions containing objects of interest using tracking, then processes each segment independently for classification. This approach enables efficient discrete object classification without iterative searching, as the segmentation step already identifies and isolates each object region for direct classification processing.
Data Source
AI summary
A method for classifying at least one object of interest in a video is provided. The method includes accessing, using at least one processing device, a frame of the video, the frame including at least one object of interest to be classified, performing, using the at least one processing device, object detection on the frame to detect the object of interest, tracking, using the at least one processing device, the object of interest over a plurality of frames in the video over time using a persistent tracking capability, isolating, using the at least one processing device, a segment of the frame that includes the object of interest, classifying, using the at least one processing device, the object of interest by processing the segment using deep learning, and generating an output that indicates the classification of the object of interest.