Image Search System Using Predicted Trajectories for Tracking
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Solution Overview
Problem
The vast and rapidly growing pool of image content from surveillance cameras and mobile devices makes it challenging to efficiently search, identify, and track specific objects or individuals due to the unstructured nature and sheer volume of data, particularly in video surveillance and image repositories.
Innovation Solution
A distributed heterogeneous network of cameras and image databases that allows for image acquisition, localization, and tracking through a system that uses metadata and computer vision techniques for image feature extraction and classification, enabling the identification of static and dynamic points of interest, and predicting their trajectories for optimized camera network access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision based image feature extraction tools are used to classify images, then image identification accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system segments the image processing task into multiple stages: first extracting features from images, then classifying them using machine learning models, and finally retrieving relevant images based on query results. This segmentation allows each component to optimize independently, improving overall efficiency while maintaining accuracy.
Solution Approach 2:
The system performs preliminary feature extraction and classification of images before they are needed for search queries. By pre-processing and indexing images with their extracted features and classifications, the system can rapidly retrieve relevant images without performing computationally intensive analysis at query time, thus reducing processing time while maintaining high identification accuracy.
2Measurement precision
If video tracking is performed to locate moving objects over time, then object identification capability is improved, but data processing complexity and time consumption increase
Solution Approach 1:
The system extracts and isolates key features from video frames, such as object appearance, movement patterns, and spatial relationships, separating these essential characteristics from the rest of the video data. By focusing only on these extracted features for tracking and identification, the system reduces data processing complexity while maintaining high object identification capability.
Solution Approach 2:
Instead of processing every pixel and frame in detail, the system applies partial action by selectively analyzing only the most relevant visual features and temporal patterns necessary for object identification and tracking. This selective processing approach reduces computational complexity while maintaining sufficient identification accuracy.
3Measurement precision
If metadata and computer vision techniques are used for image classification, then image retrieval accuracy is improved, but system complexity increases
Solution Approach 1:
The system merges multiple data sources and processing techniques into a unified architecture, combining metadata extraction, computer vision-based feature extraction, machine learning classification, and search retrieval into an integrated system. This merging allows the components to share computational resources and work together synergistically, improving retrieval accuracy while managing system complexity through cohesive design.
Data Source
AI summary
Disclosed are computerized methods and systems for providing digital image data in which there appears a captured instance of a point of interest located within a coverage area of the systems. Exemplary systems may include at least one image repository gateway for accessing one or more image data repositories which may store digital image data of images and videos acquired from within the system coverage area. The system may also include an image scanning engine to search through and access image data from the one or more image data repositories in accordance with an image data search query, selecting stored images and videos with geolocation tags indicating a location within a spatial distance of a location parameter of the image date query. The system may perform image searches and retrievals based on manually defined search queries. The system may also include an Image search query generator configured to auto-generate an image data search query to find a moving subject appearing in a previously retrieved image, wherein the image data search query for finding and or tracking may be based on an auto-predicted route of the subject derived from a previously retrieved or selected image.