Video Data Analytics for Smart City Applications: Methods and Trends

Object Detection and Tracking: Exploring a Deep Learning Approach and Other Techniques

Author(s): Samuel Oluyemi Owoeye*, Folasade Durodola and Jethro Odeyemi

Pp: 37-53 (17)

DOI: 10.2174/9789815123708123010006

* (Excluding Mailing and Handling)


Object detection and tracking have a wide range of uses, for example, in security and surveillance systems to deter and investigate crimes, for traffic monitoring, and for communication through video sharing. In a smart city, data is continuously being obtained through various means. In terms of video data, the data collected through cameras and other digital devices need to be analyzed to derive useful information from it. Hence, the concept of object detection and tracking comes into play. This chapter looks into developing various frameworks for object detection and tracking in the context of video data. We will be working with a database of 1,939 pencil images. These images will be used to train a neural network that performs image classification tasks. Various object detection methods are implemented such as Convolution Neural Network (CNN) image classification, Canny edge detection, object detection using the Haar Cascade classifier, and background subtraction. The experiments are carried out with Python programming language, TensorFlow, and OpenCV library.

Keywords: CNN , Canny edge , Haar cascade , Image processing , Neural network , Open CV , Object detection , Python

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