TR2012-003

Object Detection & Tracking


    •  Porikli, F., Yilmaz, A., "Object Detection & Tracking" in Video Analytics for Business Intelligence, Shan, C. and Porikli, F. and Xiang, T. and Gong, S., Eds., DOI: 10.1007/​978-3-642-28598-1_1, vol. 409, pp. 3-41, Springer, January 2012.
      BibTeX TR2012-003 PDF
      • @incollection{Porikli2012jan,
      • author = {Porikli, F. and Yilmaz, A.},
      • title = {Object Detection & Tracking},
      • booktitle = {Video Analytics for Business Intelligence},
      • year = 2012,
      • editor = {Shan, C. and Porikli, F. and Xiang, T. and Gong, S.},
      • volume = 409,
      • pages = {3--41},
      • month = jan,
      • publisher = {Springer},
      • doi = {10.1007/978-3-642-28598-1_1},
      • url = {https://www.merl.com/publications/TR2012-003}
      • }
  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning

Abstract:

Detecting and tracking objects are among the most prevalent and challenging tasks that a surveillance system has to accomplish in order to determine meaningful events and suspicious activities, and automatically annotate and retrieve video content. Under the business intelligence notion, an object can be a face, a head, a human, a queue of people, a crowd as well as a product on an assembly line. In this chapter we introduce the reader to main trends and provide taxonomy of popular methods to give an insight to underlying ideas as well as to show their limitations in the hopes of facilitating integration of object detection and tracking for more effective business oriented video analytics.

 

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