亞洲知識產權資訊網為知識產權業界提供一個一站式網上交易平台,協助業界發掘知識產權貿易商機,並與環球知識產權業界建立聯繫。無論你是知識產權擁有者正在出售您的知識產權,或是製造商需要購買技術以提高操作效能,又或是知識產權配套服務供應商,你將會從本網站發掘到有用的知識產權貿易資訊。

Deep Learning Object Recognition Algorithm for Video

標題
Distributive Hierarchical Model for Object Recognition in Video.
詳細技術說明
None
*Abstract

Architecture Extends Content-Based Retrieval to Video without Prior Training

This object recognition algorithm is capable of discriminating objects in videos without requiring extensive training as do most available methods. Based on a deep learning architecture normally developed for images, it provides video processing and object tracking to aid in computer vision applications such as self-driving cars, automated military drones, and surveillance. In 2012, the computer vision market was valued at $4.37 billion. Automated object recognition is classically a complex field requiring the specification of the large number of variations an object can have in an environment, including position, rotation, and scale. Many object recognition algorithms capture and process the entire image at once, losing the finer detail and requiring high processing power. Researchers at the University of Florida have developed an unsupervised object recognition algorithm in video that doesn’t require extensive training. The algorithm narrows the targeted data, reducing the amount of information and power necessary for processing.

Application

Algorithm for natural object recognition in video

Advantages

  • Capable of learning model parameters, requiring no human intervention for training
  • Extends content-based retrieval to natural video, aiding computer vision applications
  • Uses time to disambiguate images, resulting in higher quality performance

Technology

This object recognition software is based on deep learning but uses a dynamic model to handle video processing. Therefore it is capable of processing the large numbers of variations an object can have in an environment. The variations include scale, rotation, position, etc. The model sparsely represents the observations, analyzes parts of the input data independently and combines them in a hierarchical fashion with top down information. The inputs from the images are processed before being combined to form a globally invariant representation. These invariant representations can then be fed to a classifier for robust object recognition.
*IP Issue Date
Jan 3, 2017
*IP Publication Date
Nov 12, 2015
*Principal Investigation

Name: Jose Principe

Department:


Name: Rakesh Chalasani

Department:

申請日期
Dec 1, 2014
申請號碼
9,536,177
其他
國家/地區
美國

欲了解更多信息,請點擊 這裡
移動設備