4,330 research outputs found
Video matching using DC-image and local features
This paper presents a suggested framework for video matching based on local features extracted from the DCimage of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the realtime margin. There are also various optimisations that can be done to improve this computation complexity
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications
Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications
AN INVESTIGATION OF DIFFERENT VIDEO WATERMARKING TECHNIQUES
Watermarking is an advanced technology that identifies to solve the problem of illegal manipulation and distribution of digital data. It is the art of hiding the copyright information into host such that the embedded data is imperceptible. The covers in the forms of digital multimedia object, namely image, audio and video. The extensive literature collected related to the performance improvement of video watermarking techniques is critically reviewed and presented in this paper. Also, comprehensive review of the literature on the evolution of various video watermarking techniques to achieve robustness and to maintain the quality of watermarked video sequences
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