Multisensor Video Object Identification via Embedding Clustering
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Solution Overview
Problem
Conventional computer vision systems face challenges in identifying objects, especially faces, in video footage without adequate training images, leading to inaccurate and time-consuming processes due to difficulties in handling variations in appearance and false positives.
Innovation Solution
A multisensor processing platform using neural networks for real-time object recognition, which includes a face detector and embedding network to generate representative images of individuals or objects, enabling efficient identification and tracking within unstructured data by employing supervised, semi-supervised, and unsupervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computer vision systems use clustering to select a single picture from video footage to represent each face, then the system can process video data, but the identification accuracy becomes highly inaccurate due to inability to handle variations in head angle, position, lighting, and shadowing
Solution Approach 1:
The system segments the video footage into multiple frames and processes each frame independently to extract facial embeddings. Instead of selecting a single representative picture, it analyzes multiple pictures and clusters them into groups based on facial similarity, then selects the best representative from each cluster. This segmentation allows the system to handle variations in appearance across different frames while maintaining processing efficiency.
Solution Approach 2:
The patent transforms 2D facial images into high-dimensional embedding vectors (e.g., 128-dimensional or 512-dimensional spaces) using neural networks. This dimensional transformation enables the system to capture complex facial variations, lighting conditions, and head angles in a compressed representation space, where similar faces converge to nearby points regardless of their appearance variations in the original image space.
2Measurement precision
If conventional systems process all images of faces or objects in video footage, then comprehensive analysis is achieved, but significant time is required to process the volume of images
Solution Approach 1:
The system extracts only the essential information from each facial image by converting it into a compact embedding vector that captures the most salient facial features. By working with these compressed embeddings rather than the full original images, the system maintains comprehensive analysis capability while dramatically reducing the computational burden and processing time required to analyze large volumes of video data.
Solution Approach 2:
The system performs preliminary processing by pre-computing embeddings for all detected faces and objects in the video before conducting the final identification and clustering operations. This preliminary action of embedding extraction creates a condensed representation that can be rapidly processed and compared, significantly accelerating the overall analysis process while maintaining completeness.
3Reliability
If conventional facial recognition systems map all images of the same person to exactly the same point in multidimensional space, then consistent identification is achieved, but the system generates false positives when embeddings of different people are closer than embeddings of the same person
Solution Approach 1:
The system introduces dynamic thresholding and adaptive clustering that allows embedding distances to vary based on contextual factors such as lighting conditions, head angles, and image quality. Instead of using a fixed threshold for identification, the system dynamically adjusts comparison criteria based on the specific characteristics of each frame, enabling more accurate distinction between similar and different faces while reducing false positives.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its embedding representations and clustering results based on validation data and operator feedback. By iteratively improving the embedding models and adjusting clustering parameters based on performance metrics, the system reduces false positives and improves the accuracy of distinguishing between different individuals while maintaining consistent identification of the same person.
Data Source
AI summary
A multisensor processing platform includes, in at least some embodiments, a face detector and embedding network for analyzing unstructured data to detect, identify and track any combination of objects (including people) or activities through computer vision algorithms and machine learning. In some embodiments, the unstructured data is compressed by identifying the appearance of an object across a series of frames of the data, aggregating those appearances and effectively summarizing those appearances of the object by a single representative image displayed to a user for each set of aggregated appearances to enable the user to assess the summarized data substantially at a glance. The data can be filtered into tracklets, groups and clusters, based on system confidence in the identification of the object or activity, to provide multiple levels of granularity.