IJSRSET1848148 | Received : 15 June 2018 | Accepted : 26 June 2018 | May-June-2018 [(4) 8 : 640-645] © 2018 IJSRSET | Volume 4 | Issue 8 | Print ISSN: 2395-1990 | Online ISSN : 2394-4099 Themed Section : Engineering and Technology 640 An Efficient Algorithm for Real-Time Object Detection in Images Md. Samiul Islam, Samia Sultana Stamford University Bangladesh, Dhaka, Bangladesh ABSTRACT In this paper we have proposed an algorithm for object detection in various situations. Nowadays object detection and recognition has entered in every sphere of life in one or the other form. Applications of object detection are video surveillance, anti-theft system using cameras, face-recognition, biometric verification etc. Research are going on how to improve the performance in term of space and time complexity, how to deal with adverse conditions like improper lightning conditions, scene clutter, occlusion etc. and to reduce false positive rate etc. In this paper we have explained how to deal with the any situation while acquiring the images so that it can be used for better scene interpretation. Results have been generated using flash of light and dark region present in the image as some of the adverse situations. Here we have trained the system to detect the object using our algorithm. The algorithm is simple and very useful as it reduces the false positive rate as compared to contemporary algorithms and increases the efficiency of applications like video surveillance and scene interpretation etc. Keywords: Object Detection, Image Classification, Image Recognition, Histogram of Oriented Gradients, Support Vector Machine. I. INTRODUCTION An image recognition algorithm takes an image (or a patch of an image) as input and outputs what the image contains. In other words, the output is a class label (e.g. “cat”, “dog”, “table” etc.). How does an image recognition algorithm know the contents of an image? Well, we have to train the algorithm to learn the differences between different classes. If we want to find cats in images, we need to train an image recognition algorithm with thousands of images of cats and thousands of images of backgrounds that do not contain cats. Needless to say, this algorithm can only understand objects / classes it has learned. To simplify things, we will focus only on two-class (binary) classifiers. One may think that this is a very limiting assumption, but many popular object detectors (e.g. face detector and pedestrian detector) have a binary classifier under the hood. E.g. inside a face detector is an image classifier that says whether a patch of an image is a face or background. The following diagram illustrates the steps involved in a traditional image classifier. Figure 1: Object (cat) detection using traditional image processing Interestingly, many traditional computer vision image classification algorithms follow this pipeline, while