NDLI: Dictionary learning based object detection and counting in traffic scenes

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Dictionary learning based object detection and counting in traffic scenes

Content Provider	ACM Digital Library
Author	Morellas, Vassilios Papanikolopoulos, Nikolaos Lotfallah, Osama Somasundaram, Guruprasad Park, Youngchoon Sivalingam, Ravishankar
Abstract	The objective of object recognition algorithms in computer vision is to quantify the presence or absence of a certain class of objects, for e.g.: bicycles, cars, people, etc. which is highly useful in traffic estimation applications. Sparse signal models and dictionary learning techniques can be utilized to not only classify images as belonging to one class or another, but also to detect the case when two or more of these classes co-occur with the help of augmented dictionaries. We present results comparing the classification accuracy when different image classes occur together. Practical scenarios where such an approach can be applied include forms of intrusion detection i.e., where an object of class B should not co-occur with objects of class A. An example is when there are bicyclists riding on prohibited sidewalks, or a person is trespassing a hazardous area. Mixed class detection in terms of determining semantic content can be performed in a global manner on downscaled versions of images or thumbnails. However to accurately classify an image as belonging to one class or the other, we resort to higher resolution images and localized content examination. With the help of blob tracking we can use this classification method to count objects in traffic videos. The method of feature extraction illustrated in this paper is highly suited to images obtained in practical cases, which are usually of poor quality and lack enough texture for the popular gradient based methods to produce adequate feature points. We demonstrate that by training different types of dictionaries appropriately, we can perform various tasks required for traffic monitoring.
Starting Page	42
Ending Page	48
Page Count	7
File Format	PDF
ISBN	9781450303170
DOI	10.1145/1865987.1865995
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-08-31
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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