Object Enumeration from Video Sequences

Abstract

The computer vision community has expended a great amount of effort in recent years towards the goal of counting people in videos. Much more recently, algorithms have been developed to identify objects in videos robustly. The goal of this project is to implement a system based on one of those algorithms, in order to count the objects in an offline video. The system addresses the problem of counting the number of objects in an image frame. This system presents a human detection model, that is designed to work with people . The system proposed does learning through templates. The model makes use of Haar based features to form templates performs matching of Haar-transformed images. Objects can be detected irrespective of the texture and color of there clothing as well as orientation. This system attempts to provide a Wavelet based human detection system. Human beings are non rigid objects and as such deteting them is a hard problem, due to the various possible combinations that arise out of clothes being worn, there texture, the orientation of the individual. To overcome this, we need a systems that is invariant to the colour differences, this is made possible by using Haar transforms. These have the property that they extract information from a given image, which is invariant to the absolute colour, and makes use of only color changes. The problem of handling multiple orientatios can be tackled by having a sufficiently large database of people in different orientations. Having a learning system simplifies the task of adding more templates as and when needed to handle new cases that may arise. Multi resolution Haar transform were found for human templates and Pyramidal search was caried out to match human beings. Human detection and counting has numerous advantages in real life problems.