A Novel Lightweight Approach for Video Retrieval on Mobile Augmented Reality Environment

Abstract

Mobile Augmented Reality merges the virtual objects with real world on mobile devices, while video retrieval brings out the similar looking videos from the large-scale video dataset. Since mobile augmented reality application demands the real-time interaction and operation, we need to process and interact in real-time. Furthermore, augmented reality based virtual objects can be poorly textured. In order to resolve the above mentioned issues, in this research, we propose a novel, fast and robust approach for retrieving videos on the mobile augmented reality environment using an image and video queries. In the beginning, Top-K key-frames are extracted from the videos which significantly increases the efficiency. Secondly, we introduce a novel frame based feature extraction method, namely Pyramid Ternary Histogram of Oriented Gradient (PTHOG) to extract the shape feature from the virtual objects in an effective and efficient manner. Thirdly, we utilize the Double-Bit Quantization (DBQ) based hashing to accomplish the nearest neighbor search efficiently, which produce the candidate list of videos. Lastly, the similarity measure is performed to re-rank the videos which are obtained from the candidate list. An extensive experimental analysis is performed in order to verify our claims.

Virtual Object Manipulation by Combining Touch and Head Interactions for Mobile Augmented Reality

“…Currently, owing to the development of computer hardware, several commercial head-mounted displays (HMDs) [2][3][4][5] are available, which a user can use to experience AR in a mobile environment. Providing room modeling using the simultaneous localization and mapping (SLAM) algorithm, some HMDs [3,4] allow a user to interact with virtual contents [6]. Especially, a virtual interior task [7,8], which adds virtual furniture in a real room, requires an interaction method for repeatedly and conveniently manipulating the remote virtual object in the AR environment.…”

Fast Fallback Watermark Detection Using Perceptual Hashes

“…Discriminant means that two perceptually different videos should have significantly different hashes. Although most perceptual hashing research was performed on images [30][31][32][33], video copy detection has been investigated as well [34][35][36][37][38][39][40].…”