Visual Object Tracking with Lingual Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional visual object tracking systems lack semantic information, making it challenging to track objects with specific visual constraints in videos.
Innovation Solution
A computer-implemented method and apparatus that incorporates lingual constraints, such as natural language sentences, into the tracking process by generating word embeddings, extracting video frame features, cross-correlating the embeddings with the features, and generating predictions based on the cross-correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional visual object tracking systems are used, then motion trajectory information can be obtained, but semantic information is absent making tracking with visual constraints challenging
Solution Approach 1:
The patent merges visual feature extraction with linguistic constraint processing into a unified tracking framework. The system combines CNN-based visual feature extraction with word embedding-based linguistic constraint representation, allowing simultaneous processing of both visual and semantic information within a single tracking pipeline that cross-correlates these features to identify target objects.
Solution Approach 2:
The patent introduces cross-correlation as an intermediary mechanism that bridges visual features and linguistic constraints. This intermediary process computes similarity between extracted visual features and constraint-based feature templates, enabling the system to match visual objects with their corresponding linguistic descriptions without requiring direct integration of complex semantic parsing.
2Measurement precision
If deep neural networks are used for object tracking, then accurate object detection and tracking can be achieved, but computational time increases and computational costs become problematic for mobile devices
Solution Approach 1:
The patent segments the tracking process into distinct modular components: visual feature extraction using CNNs, linguistic constraint processing using word embeddings, and cross-correlation matching. This segmentation allows each component to be optimized independently and enables selective application of computationally intensive operations only when needed, reducing overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by using lightweight CNN architectures and pre-computed word embeddings for constraints. Instead of performing full deep neural network inference on every video frame, the system extracts essential visual features and matches them against pre-processed linguistic constraint representations, performing computations only to the extent necessary for accurate tracking.
Data Source
AI summary
A computer-implemented method for tracking with visual object constraints includes receiving a lingual constraint and a video. A word embedding is generated based on the lingual constraint. A set of features is extracted for one or more frames of the video. The word embedding is cross-correlated to the set of features for the one or more frames of the video. A prediction indicating whether the lingual constraint is in the one or more frames of the video is generated based on the cross-correlation.