Browsing by Author "Joshi, Ajay J."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Data Mining of Traffic Video Sequences
    (University of Minnesota Center for Transportation Studies, 2009-09) Joshi, Ajay J.; Papanikolopoulos, Nikolaos
    Automatically analyzing video data is extremely important for applications such as monitoring and data collection in transportation scenarios. Machine learning techniques are often employed in order to achieve these goals of mining traffic video to find interesting events. Typically, learning-based methods require significant amount of training data provided via human annotation. For instance, in order to provide training, a user can give the system images of a certain vehicle along with its respective annotation. The system then learns how to identify vehicles in the future - however, such systems usually need large amounts of training data and thereby cumbersome human effort. In this research, we propose a method for active learning in which the system interactively queries the human for annotation on the most informative instances. In this way, learning can be accomplished with lesser user effort without compromising performance. Our system is also efficient computationally, thus being feasible in real data mining tasks for traffic video sequences.
  • Thumbnail Image
    Item
    Freeway Network Traffic Detection and Monitoring Incidents
    (Minnesota Department of Transportation, 2007-10) Joshi, Ajay J.; Atev, Stefan; Fehr, Duc; Drenner, Andrew; Bodor, Robert; Masoud, Osama; Papanikolopoulos, Nikolaos P.
    We propose methods to distinguish between moving cast shadows and moving foreground objects in video sequences. Shadow detection is an important part of any surveillance system as it makes object shape recovery possible, as well as improves accuracy of other statistics collection systems. As most such systems assume video frames without shadows, shadows must be dealt with beforehand. We propose a multi-level shadow identification scheme that is generally applicable without restrictions on the number of light sources, illumination conditions, surface orientations, and object sizes. In the first level, we use a background segmentation technique to identify foreground regions that include moving shadows. In the second step, pixel-based decisions are made by comparing the current frame with the background model to distinguish between shadows and actual foreground. In the third step, this result is improved using blob-level reasoning that works on geometric constraints of identified shadow and foreground blobs. Results on various sequences under different illumination conditions show the success of the proposed approach. Second, we propose methods for physical placement of cameras in a site so as to make the most of the number of cameras available.
  • Thumbnail Image
    Item
    Multi-Camera Monitoring of Human Activities at Critical Transportation Infrastructure Sites
    (University of Minnesota Center for Transportation Studies, 2008-06) Ribnick, Evan; Joshi, Ajay J.; Papanikolopoulos, Nikolaos P.
    The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. In this report, we consider the problem of counting the number of people in the scene and also tracking them reliably. We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in real time. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Favorable results are shown for groups of various sizes moving in an unconstrained fashion.