Open Set Brand Logo Detection in Video Frames
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Solution Overview
Problem
Conventional video processing systems for detecting brand logos in videos require retraining machine learning models, which is time-consuming and inconvenient, especially when adding new logos, as they need to be trained on specific samples and are slow to adapt to new or modified objects.
Innovation Solution
A system and method for fast and adaptive brand logo detection using an open set approach, involving a video splitter, localization, cropping, classification, and comparison modules, with self-supervised learning and few-shot learning, allowing for on-the-fly training and detection of brand logos, taglines, and ambassador images without pre-training on target frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine vision models are trained on specific logo samples, then detection accuracy for those logos is improved, but the time required to add new logos increases significantly due to retraining requirements
Solution Approach 1:
The system pre-trains the machine learning model on a diverse dataset of logos before deployment. This preliminary training enables the model to recognize various logo patterns and styles in advance, so when new logos need to be detected, the system can quickly adapt using few-shot learning rather than requiring extensive retraining from scratch.
Solution Approach 2:
The system changes the training approach by implementing few-shot learning capability, where the model can learn to detect new logos with only a small number of sample images (e.g., 5-10 samples). This parameter change in the learning methodology dramatically reduces the time required to adapt to new logos while maintaining high detection accuracy.
2Reliability
If the system is trained on extensive logo samples, then detection reliability is improved, but the complexity of the training process and system setup increases
Solution Approach 1:
The system implements self-service through automated few-shot learning, where the model can automatically adapt to new logos by processing a small number of provided samples without requiring complex manual training procedures. The system handles the entire adaptation process autonomously, from receiving sample images to updating detection capabilities, thereby reducing operational complexity while maintaining high reliability.
3Measurement precision
If conventional detection systems are used, then detection accuracy for known logos is maintained, but the adaptability to new or modified logos is poor
Solution Approach 1:
The system implements dynamic adaptability by enabling the machine learning model to continuously learn and update its detection capabilities. Through few-shot learning, the model can dynamically adjust to new logos and modified logo styles in real-time, transforming the system from a static detector to a dynamically adaptable one that maintains high accuracy across evolving logo designs.
Solution Approach 2:
The system achieves universality by designing a machine learning model that can detect multiple types of logos across different industries, styles, and formats. The few-shot learning capability enables the same model to adapt to various logo types (brand logos, product logos, service logos) without requiring separate specialized models, thereby providing versatile detection across diverse applications.
Data Source
AI summary
A system and method for performing brand detection in a video is disclosed herein. The method comprises receiving the video for performing the brand detection thereon; splitting the video for obtaining a plurality of video frames; performing an open set detection on each input video frame from the plurality of video frames, which comprises proposing one or more bounding boxes on the input video frames on regions of the video frame that potentially include brand media; cropping the one or more bounding boxes; providing the cropped bounding boxes to a classification module for obtaining embedding vectors corresponding to each of the cropped bounding boxes; and comparing the embedding vectors of the cropped bounding boxes with embedding vectors of one or more brand reference images provided by a user for computing instances of brand detection in each video frame of the plurality of video frames.