时间:7月5日 9:30-10:30     地点:教十八223室

Dr. Jonathan Wu

Professor and Canada Research Chair (Tier 1)
Department of Electrical and Computer Engineering
University of Windsor, Canada

Analytic Training Approach for Object/Action Recognition

The computer vision community is faced with the challenge of devising novel, robust and efficient algorithms to learn models which are helpful in categorizing huge amount of visual data.  Recognition algorithms play a pivotal role in commercially available frameworks for automated analysis and detection of objects of interest (OI). Traditionally, supervised learning frameworks have inherent limitations of longer durations for training and finding local maxima that may lead to poor classification accuracy. Recent action recognition schemes have ignored complexity associated with redundant training samples and learning strategies. To cope with inherent limitations of gradient descent approach, model learning can be analytically performed at an extremely fast speed without iterative adjustments. This presentation is mainly concentrated on recent trends in action and activity recognition. We believe that computation, and selection of meaningful features and their use in model learning are equally important for recognition purposes. In this presentation, first we will discuss reasons for analytic training approach and then present a general framework to efficiently identify OI in still images and later extend its application to human action recognition in videos as well. Such scheme can also be implemented in a situation where training data is coming in a serial mode and training needs to be performed in an incremental fashion.

时间:7月5日 10:30-11:30     地点:教十八 223室

Dr. Wei Zhang
Department of Electrical and Computer Engineering
University of Windsor, Canada

题目：交通监控系统中的关键技术与工程应用
摘要：基于视觉的智能交通系统利用图像处理、模式识别等理论提取交通监控场景中的车辆及行人信息，这些信息在缓解城市交通拥堵、提高道路使用效率、规范驾驶行为、减少空气污染等方面发挥了重要作用。交通智能监控系统已经成为道路交通管理的基本手段，具有广泛的应用。在这个报告中，将阐述近年来该领域发展的动态，我们在车辆信息获取与分析方面的进展，以及未来交通监控系统的发展方向。