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Video scalability is a recent video coding technology that allows content providers to offer multiple quality versions from a single encoded video file in order to target different kinds of end-user devices and networks. One form of scalability utilizes the region-of-interest concept, that is, the possibility to mark objects or zones within the vid...
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... difference with more traditional video encod- ing schemes is a novel approach to encode the residual val- ues. The FGS bit-plane DCT residue value encoding occurs by zig-zag scanning the values and by placing them in their binary form in a matrix (Figure 2). Note that the sign of a value is stored separately, so only the absolute value is used for the binary representation. ...
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... Then, each selected macroblock is tracked in a frame by frame basis by using its motion vectors projection. In [6], a macroblock tracking technique improves [5] by creating two independent layers on the top of the macroblock grid, thereby allowing a more fine grained tracking of object boundaries with resolution superior to macroblock size. ...
A novel Temporal Motion Vector Filter (TF) is presented and evaluated for real-time object detection on com- pressed videos in MPEG-2, MPEG-4 or H.264/AVC formats. The filter significantly reduces the noisy motion vectors that do not represent a real object movement . The filter analyses the temporal coherence of block motion vectors to determine if they are likely to represent true motion in the recorded scene.
Experiments are performed using the CLEAR metrics for object detection and public available datasets from CAVIAR, PETS and CLEAR. These experiments demonstrate that the TF outperforms the Vector Median Filter, by providing better object detection accuracy with reduced computational complexity.
The good results obtained by the TF make it suitable as a first step towards implementing systems that aim to detect and track objects from compressed video by using motion vectors. The TF could also be used to improve other techniques based on motion vectors such as Global Motion Estimation (GME) and Motion-Compensated Frame Interpolation (MCFI).
... Based on relations (6)(7)(8) and on the bandwidth model (12), we would get (14) and (16), we can plot the probability (10) in Fig. 19. For a deviation d 0 = 50 ms, an upswitch has a 90% success rate provided the streaming rate R is lower than 300 kbps in 3G networks, 500 kbps in WiMAX scenario, 1 Mbps in WiFi b and 5 Mbps in WiFi g. ...
This paper describes an interactive and adaptive streaming architecture that exploits temporal concatenation of H.264/AVC video bit-streams to dynamically adapt to both user commands and network conditions. The architecture has been designed to improve the viewing experience when accessing video content through individual and potentially bandwidth constrained connections. On the one hand, the user commands typically gives the client the opportunity to select interactively a preferred version among the multiple video clips that are made available to render the scene, e.g. using different view angles, or zoomed-in and slow-motion factors. On the other hand, the adaptation to the network bandwidth ensures effective management of the client buffer, which appears to be fundamental to reduce the client-server interaction latency, while maximizing video quality and preventing buffer underflow. In addition to user interaction and network adaptation, the deployment of fully autonomous infrastructures for interactive content distribution also requires the development of automatic versioning methods. Hence, the paper also surveys a number of approaches proposed for this purpose in surveillance and sport event contexts. Both objective metrics and subjective experiments are exploited to assess our system. Index Terms— interactive streaming, clip versioning, RoI extraction, bitrate adaption, H.264/AVC.
... In [13], an approach was proposed that adds higher level elements such as motion field correction filtering in the context of H.263. In [14], a method of using automatic resizing of ROIs detected by video encoder motion estimation in conjunction with object tracking is presented; for this algorithm an effective ROI estimate requires encoder motion estimation capturing true motion. In [15], a method of using ROIs to focus limited processing power on highest gain encoder components in the context of H.264/AVC is presented. ...
... However, instead of a single high bitrate video source captured from a remote node, raw content previously acquired from various cameras with different visibility conditions and viewing angles is used. In order to implement gQT we modify the QT search described in Section V by replacing (14) by ...
In centralized transportation surveillance systems, video is captured and compressed at low processing power remote nodes and transmitted to a central location for processing. Such compression can reduce the accuracy of centrally run automated object tracking algorithms. In typical systems, the majority of communications bandwidth is spent on encoding temporal pixel variations such as acquisition noise or local changes to lighting. We propose a tracking-aware, H.264-compliant compression algorithm that removes temporal components of low tracking interest and optimizes the quantization of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that our algorithm allows for over 90% bitrate savings while maintaining comparable tracking accuracy.
... [6] a method of using automatic resizing of ROIs detected by video encoder motion estimation in conjunction with object tracking is presented, where the ROI detection relies on motion estimation capturing true motion (and not for example best block match) for good results. In [11] a method of using ROIs to focus limited processing power on highest gain encoder components in the context of H.264 is presented. ...
... The subject of standard-compliant video compression specifically optimized for later tracking has been explored as early as [5] in the context of MPEG which focuses on concentrating (consolidating ) bitrate on a Region of Interest (ROI). More recently in [9] a more elaborate approach that adds higher level elements such as motion field correction filtering is proposed in the context of H.263. In [6] a method of using automatic resizing of ROIs detected by video encoder motion estimation in conjunction with object tracking is presented, where the ROI detection relies on motion estimation capturing true motion (and not for example best block match) for good results. In [11] a method of using ROIs to focus limited processing power on highest gain encoder components in the context of H.264 is presented. ...
... A few approaches specific to MPEG-4 and H.264-AVC/SVC have been proposed in the literature. Sutter et al. [22] presented a lightweight tracking algorithm for MPEG-4/FGS. No indication for the performance in the case of multiple occluding objects is given and the system has to be initialized by the user. ...
We present methods to efficiently analyze scalable, compressed H.264/scalable video coding (SVC) video streams. Relying solely on information present in the compressed stream, we estimate the global camera motion, perform motion segmentation and use a simple matching process to track moving objects over time. Object energy images are constructed in order to help resolve the problem of object correspondence during the occlusions of multiple objects. To save computing time, we analyze lower spatial layers of the stream and add higher layer information only if necessary. We draw 2-D object trajectories in the view plane of the camera and use the temporal evolution of the objects’ properties to estimate the relative distance to the camera, resulting in a pseudo 3-D representation of the trajectories. Finally, the suitability of the motion parameters to perform video retrieval/copy detection tasks is demonstrated. We therefore form two simple descriptors that are invariant to a series of transformations.
... A few approaches specific to MPEG-4 and H.264-AVC/SVC have been proposed in the literature. Sutter et al. [16] presented a lightweight tracking algorithm for MPEG-4/FGS. No indication for the performance in the case of multiple occluding objects are given and the system has to be initialized by the user. ...
This paper presents a simple and fast method for unsupervised trajectory estimation of multiple moving objects within a video scene. It is entirely based on the motion vectors that are present in compressed H.264/AVC or SVC video streams. We extract these motion vectors, perform robust frame-wise global motion estimation and use these estimates to form outlier masks. Motion segmentation on the spatio-temporally filtered outlier masks is performed to detect moving regions in the scene, which are analyzed over time in order to identify similar objects in adjacent frames. The construction of so-called Object History Images (OHIs) is proposed to stabilize the trajectories, which are finally interpolated with X-splines. The system enables real-time analysis with standard hardware.
In this paper, we present a transportation video coding and wireless transmission system specifically tailored to automated vehicle tracking applications. By taking into account the video characteristics and the lossy nature of the wireless channels, we propose video preprocessing and error control approaches to enhance tracking performance while conserving bandwidth resources and computational power at the transmitter. Compared with current state-of-the-art H.264-based implementations, our system is shown to yield over 80% bitrate savings for comparable tracking accuracy.
In this paper, a novel filter for real-time object trackingfrom compressed domain is presented and evaluated. Thefilter significantly reduces the noisy motion vectors, that donot represent a real object movement, from Mpeg familycompressed videos. The filter analyses the spatial (neighborhood)and temporal coherence of block motion vectorsto determine if they are likely to represent true motion fromthe recorded scene. Qualitative and quantitative experimentsare performed displaying that the proposed spatiotemporalfilter (STF) outperforms the currently widelyused vector median filter. The results obtained with the spatiotemporalfilter make it suitable as a first step of any systemthat aims to detect and track objects from compressedvideo using its motion vectors.
We present a novel system to perform efficient, compressed domain aided video analysis in the context of traffic surveillance applications. After camera installation, the system initializes by performing two short and fully automatic learning stages to gather information about the background and the principal moving directions in the scene. This knowledge is later used to assist the detection and tracking of vehicles. We combine processing in the pixel domain on decoded I-frames with motion based information from the H.264/SVC compressed domain in order to obtain a hybrid solution that delivers robust results at low computational complexity. Pan-tilt-zoom cameras are supported by the system, since global motion estimation is performed using the motion vectors that are present in the compressed stream.
