This paper presents a moving object detection algorithm for H.264/AVC video streams that is applied in the compressed domain. The method is able to extract and analyze several syntax elements from any H.264/AVC-compliant bit stream. The number of analyzed syntax elements depends on the mode in which the method operates. The algorithm is able to perform either a spatiotemporal analysis in a single step or a two-step analysis that starts with a spatial analysis of each frame, followed by a temporal analysis of several subsequent frames. Thereby, in each mode either only (sub-)macroblock types and partition modes or, additionally, quantization parameters are analyzed. The evaluation of these syntax elements enables the algorithm to determine a “weight” for each 4×4 block of pixels that indicates the level of motion within this block. A final segmentation after creating these weights segments each frame to foreground and background and hence indicates the positions and sizes of all moving objects. Our experiments show that the algorithm is able to efficiently detect moving objects in the compressed domain and that it is configurable to process a large number of parallel bit streams in real time.

In this paper, we present a novel moving object detection algorithm for H.264/AVC-compressed video streams. The algorithm does not require full decoding up to the pixel domain but only parsing the compressed bit streams. Thereby, only syntax elements for reconstructing (sub-)macroblock types and quantization parameters are extracted. These features are used to segment the video frames into foreground and background and, according to this segmentation, to identify regions containing moving objects. In a first step, (sub-)macroblock types are analyzed to create initial maps indicating for each block the “weight” for the presence of a moving object. These maps serve as input for our novel spatio-temporal detection algorithm to refine the weight indicating the level of motion for each block. Finally, quantization parameters of macroblocks are used to apply individual thresholds to the block weights to segment the video frames. Experimental results show that our approach efficiently identifies regions containing moving objects and that the presented algorithm is suitable for processing a large number of video streams in parallel.

In this paper we present a new hybrid framework for detecting and tracking persons in surveillance video streams compressed according to the H.264/AVC video coding standard. The framework consists of three stages and operates in both the compressed and the pixel domain of the video. The combination of compressed and pixel domain represents the hybrid character. Its main objective is to significantly reduce the amount of computation required, in particular for frames and image regions with few people present. In its first stage the proposed framework evaluates the header information for each compressed frame in the video sequence, namely the macroblock type information. This results in a coarse binary mask segmenting the frame into foreground and background. Only the foreground regions are processed further in the second stage that searches for persons in the image pixel domain by applying a person detector based on the Implicit Shape Model. The third stage segments each detected person further with a newly developed method that fuses information from the first two stages. This helps obtaining a finer segmentation for calculating a color histogram suitable for tracking the person using the mean shift algorithm. The proposed framework was experimentally evaluated on a publicly available test set. The results demonstrate that the proposed framework reliably separates frames with and without persons such that the computational load is significantly reduced while the detection performance is kept.

This paper introduces a low complexity frame-based object detection algorithm for H.264/AVC video streams. The method solely parses and evaluates H.264/AVC macroblock types extracted from the video stream, which requires only partial decoding. Different macroblock types indicate different properties of the video content. This fact is used to segment a scene in foreand background or, more precisely, to detect moving objects within the scene. The main advantage of this algorithm is that it is most suitable for massively parallel processing, because it is very fast and combinable with several other preand post-processing algorithms, without decreasing their performance. The actual algorithm is able to process about 3600 frames per second of video streams in CIF resolution, measured on an Intel R CoreTM i5-2520M CPU @ 2.5 GHz with 4 GB RAM.

This paper presents a novel change detection algorithm for the compressed domain. Many video surveillance systems in practical use transmit their video data over a network by using the Real-time Transport Protocol (RTP). Therefore, the presented algorithm concentrates on analyzing RTP streams to detect major changes within contained video content. The paper focuses on a reliable preselection for further analysis modules by decreasing the number of events to be investigated. The algorithm is designed to work on scenes with mainly static background, like in indoor video surveillance streams. The extracted stream elements are RTP timestamps and RTP packet sizes. Both values are directly accessible by efficient byte-reading operations without any further decoding of the video content. Hence, the proposed approach is codec-independent, while at the same time its very low complexity enables the use in extensive video surveillance systems. About 40,000 frames per second of a single RTP stream can be processed on an Intel ® Core™ 2 Duo CPU at 2 GHz and 2 GB RAM, without decreasing the efficiency of the algorithm.