Sentence-Guided Video Object Codetection Without Pose Data
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Solution Overview
Problem
Existing video detection systems require human pose and depth information to reduce processing time and increase accuracy, and can only detect one common object at a time, limiting their ability to handle multiple object classes and occluded or small objects in video content.
Innovation Solution
A method that determines object locations and types in videos by pairing them with descriptive sentences, using object proposal mechanisms and pretrained detectors to identify objects without relying on human pose information, and employing spatio-temporal constraints from sentences to detect multiple object classes simultaneously across video clips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human pose and depth information are used to prune search space, then detection accuracy is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent extracts and utilizes only the necessary semantic information from sentences (object types, spatial relationships, temporal patterns) while discarding unnecessary pose and depth information, thereby achieving accurate detection without the complexity of processing complete pose and depth data
Solution Approach 2:
The patent introduces sentence semantics as an intermediary representation that bridges the gap between raw video data and object detection, allowing the system to prune search space using high-level semantic constraints rather than directly processing complex pose and depth information
2Productivity
If one common object is detected at a time, then processing time is reduced, but the quantity of detected objects is limited
Solution Approach 1:
The patent merges the detection of multiple object classes into a single unified process by formulating them as a joint optimization problem, where the sentence semantics guide the simultaneous detection of all object classes in the video clip rather than processing them sequentially
Solution Approach 2:
The patent creates a universal detection framework that can handle multiple object classes simultaneously using the same sentence-based constraint mechanism, allowing a single algorithm to perform both single-object and multi-object detection without requiring separate specialized systems
3Measurement precision
If pretrained object detectors are used, then detection accuracy is improved, but adaptability to novel objects and activities is reduced
Solution Approach 1:
The patent segments the detection task into two parts: using pretrained detectors only for generating initial object proposals and using sentence semantics for the actual detection and reasoning process, this allows the system to leverage pretrained knowledge while maintaining adaptability to novel objects through semantic constraints
Solution Approach 2:
The patent changes the detection approach from using fixed pretrained categories to using flexible sentence-based semantic constraints, allowing the system to adapt to novel objects and activities by modifying the semantic parameters in the sentences rather than relying on fixed detector categories
Data Source
AI summary
A system and method for determining the locations and types of objects in a plurality of videos. The method comprises pairing each video with one or more sentences describing the activity or activities in which those objects participate in the associated video, wherein no use is made of a pretrained object detector. The object locations are specified as rectangles, the object types are specified as nouns, and sentences describe the relative positions and motions of the objects in the videos referred to by the nouns in the sentences. The relative positions and motions of the objects in the video are described by a conjunction of predicates constructed to represent the activity described by the sentences associated with the videos.