CSEIT411830 | Published - 25 April 2018 | March-April-2018 [ (4 ) 1 : 178-182 ] National Conference on Recent Advances in Computer Science and IT (NCRACIT) International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 4 | Issue 1 | ISSN : 2456-3307 178 Subspace-Based Adaptation of Detectors for Video Sangeeta Bhan 1 , Sunil Dalal 2 , Pankaj Choudhary 3 1 Computer Science and Engineering, Delhi Technological University, Delhi, India 23 Department of Information Technology BGSB University, Rajouri, J&K, India sangeetabhan7@gmail.com 1 , sunildalal57@gmail.com 2 , pschoudhary@bgsbu.ac.in 3 ABSTRACT Object detection in videos has always been a challenging problem to work with. Detection of a particular class object plays an important role in many real-world applications. Since the domain of source and target video vary significantly, classifier being trained on source video does not give expected results on the target video. Thus, domain adaptation techniques are used, one of which is Subspace Based Adaptation. In this technique, first, we compute both source and target subspace from the features collected. Since we do not have target data directly, we use different ways to get data from the target video. Compute subspace after collecting the data from both source and target videos. Eigen vectors describe this generated source and target subspaces. Keywords: Subspace, Detector, Adaptation, Histogram. I. INTRODUCTION In detection the object of the particular class, it is typically presumed that source and target data have the same distribution. However, it is not true in real world applications. We are training our simple detector model from the source video with the help of its annotation file. Initially, the problem is to collect the negatively labeled data from video. We have used Hard Negative Mining to collect this negatively labeled data. Next, we extract the features of these data using Histogram of Oriented Gradients. With the help of Background Subtraction using MoG, data samples were collected from the target video for subspace calculation. PCA is used to find the best Eigen vectors for both source and target dataset. Once we have calculated the subspaces using PCA, transforms the source subspace coordinate system into the target aligned source subspace coordinate system by aligning the source basis vectors with the target ones. Generate training data by mapping source data into this target aligned source subspace. Now, we train our linear SVM model using this new labeled data. Iteratively use online samples or bounding boxes detected on target video to generate new detector model. II. RELATED WORK AND BACKGROUND THEORY Over the past several years, many different have been pro-posed to detect objects of a particular class in videos and images. The main problem in object detection is varying domains of source and target data. So the main issue is to find out the relationship between these two domains. Domain adaptation is a widely used technique in computer vision and language processing. A classical strategy related to our work consists of learning a new domain- invariant feature representation by a new projection