Object Counting in Video Sequences

This paper addresses the problem of counting thenumber of object in an image frame. This paper presents ahuman detection model, that is designed to work with object. The system proposed does learning through templates. The modelmakes use of Haar based features to form templates performsmatching of Haar-transformed images. Object can be detectedirrespective of the texture and color of their clothing as well asorientation. Index Terms—Object detection, hear templates, object counting I. INTRODUCTION This paper attempts to provide a Wavelet based humandetection system. Human beings are non rigid objects and as such deteting them is a hard problem, due to the variouspossible 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 thecolour differences, this is made possible by using Haar transforms. These have the property that they extract informationfrom a given image, which is invariant to the absolute colourand makes use of only color changes. The problem of handlingmultiple orientatios can be tackled by having a sufficientlylarge database of object in different orientations. Having alearning system simplifies the task of adding more templatesas 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 humanbeings. Human detection and counting has numerous advantages in real life problems. Some of the major applicationsareHuman Intrusion detection. • Tracking usage of Resource / Preferences of object. (Unobtrusive monitoring) • Optimizing working of road crossing signals. • Getting a rough count of the number of object in anenclosed area (malls & bank).