Video Registration in Egocentric Vision under Day and Night Illumination Changes

With the spread of wearable devices and head mounted cameras, a wide range of application requiring precise user localization is now possible. In this paper we propose to treat the problem of obtaining the user position with respect to a known environment as a video registration problem. Video registration, i.e. the task of aligning an input video sequence to a pre-built 3D model, relies on a matching process of local keypoints extracted on the query sequence to a 3D point cloud. The overall registration performance is strictly tied to the actual quality of this 2D-3D matching, and can degrade if environmental conditions such as steep changes in lighting like the ones between day and night occur. To effectively register an egocentric video sequence under these conditions, we propose to tackle the source of the problem: the matching process. To overcome the shortcomings of standard matching techniques, we introduce a novel embedding space that allows us to obtain robust matches by jointly taking into account local descriptors, their spatial arrangement and their temporal robustness. The proposal is evaluated using unconstrained egocentric video sequences both in terms of matching quality and resulting registration performance using different 3D models of historical landmarks. The results show that the proposed method can outperform state of the art registration algorithms, in particular when dealing with the challenges of night and day sequences

Very low bit-rate video coding focusing on moving regions using three-tier arbitrary-shaped pattern selection algorithm

Very low bit-rate video coding using patterns to represent moving regions in macroblocks exhibits good potential for improved coding efficiency. Recently an Arbitrary Shaped Pattern Selection (ASPS) algorithm and its Extended version(EASPS) were presented, that used a dynamically extracted set of patterns, of the two different sizes, based on actual video content. These algorithms, like other pattern matching algorithms failed to capture a large number of active-region macroblocks (RMB) especially when the object moving regions is relatively larger in a video sequence. As the size of the moving object may vary, superior coding performance is achievable by using dynamically extracted patterns of a larger size. This paper, proposes a three-tier Arbitrary Shaped Pattern Selection (ASPS-3) algorithm that uses three different pattern sizes for very low bit ate coding. Experimental results show that ASPS-3 exhibits better performance compared with other pattern matching algorithms, including the low-bit rate video coding standard H.263

Using segmented objects in ostensive video shot retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partitioning of a user’s search into two or more distinct search threads, which can be followed by the user in sequence. This is an automatic process which maps neatly to the ostensive model for information retrieval in that it allows a user to place a virtual checkpoint on their search, explore one thread or aspect of their information need and then return to that checkpoint to then explore an alternative thread. Our system is fully functional and operational and in this paper we illustrate several design decisions we have made in building it

Graph Based Video Sequence Matching & BoF Method for Video Copy detection

In this paper we propose video copy detection method using Bag-of-Features and showing acyclic graph of matching frames of videos. This include use of both local (line, texture, color) and global (Scale Invariant Feature Transform i.e. SIFT) features. This process includes dividing video into small frames using dual threshold method which eliminates the redundant frames and select unique key frames. After that from each key frame binary features are extracted which known as Bag of Features (BoF) which are get stored into the database in format of matrix. When any query video is being uploading, same features are extracted and compared with stored database to detect copied video. If video detected as copied then using Graph Based Sequence Matching Method, actual matched sequence between key frames is displayed in acyclic graph. DOI: 10.17762/ijritcc2321-8169.15067

Hybrid Approach for Video Compression Using Block Matching Motion Estimation

To discard the redundancy present in video some video compression technique are involved .Basically video is a collection sequential frames in a sequence. video compression means reducing the size of video . In video sequence there are two types of technique are present that are temporal redundancy and spatial redundancy. In this paper we discuss about hybrid technique .Hybrid means combination of any two or more than two technique like efficient three step search algorithm(E3SS) and cross hexagonal search algorithm (CHS) .In today’s date block matching algorithm for motion estimation is powerful technique for high compression ratio and to reduce computational complexity .The motion estimation calculate the position of pixel and It is a custom to calculate the pixel from current frame to reference frame .The main function of motion estimation is reducing the search point and redundancy present in video .The experiment result shows that the proposal algorithm performs better than previous proposed block matching algorithms and required less computation than other technique