Real-time implementation of foreground object detection from a moving camera using the ViBe algorithm

Abstract

The article presents a real-time hardware implementation of a foreground object detection for a non-static camera setup. The system consists of two parts: the calculation of the displacement between two consecutive frames using a correlation based corner tracker and background generation method ViBE (Visual Background Extractor). The paper discusses details of the used hardware modules, resource utilization, computing performance and power dissipation. The solution was evaluated on sequences recorded with a static and moving camera. The system was successfully tested on a hardware platform with an FPGA device. It allows to process a 720x576 pixels and 50 frames per second video stream in real-time.

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

“…Taking into account the association among ten surveillance videos, utilizing (19) and (20) [28] is employed to generate a 3D stereo from a 2D video sequence and highlight evolution of environment changes. association function (here ( ) = + ), EC-warning levels from to +1 are 1 = 0.4096, 2 = 0.0984, 3 = 0.6314, and 4 = 0.9220, respectively, = 1, 2, 3, 4.…”

“…For complex applications, however, imbedding proposed models in current security systems becomes necessary, such as compressive sensing for sparse tracking [18] (it can be improved as locally compressive sensing within ROI), VIBE algorithm for real-time object detection from a moving camera [19], Adaboost algorithm for noise-detection in ROI [20], optical flow for robots' recognition of environments [21], SVM clustering for accidents classification [22], deep learning algorithms for anomaly detection, crow analysis, and hierarchical tracking within ROI [23][24][25][26][27]. Objects understanding and detection in dynamic environment changes are usually based on the adaptive background subtraction and other objects recognition methods [17,21,35,[65][66][67][68].…”

“…Numerous algorithms have been developed to tackle video recognition challenges in various environments; however, a full understanding of environmental implications to video recognition efficiency demands learning models with universal significance (ignoring uncontrolled differences in real scenarios) [18][19][20][21][22][23][24][25][26][27]. That is the essential reason why the current online algorithms, even for latest algorithms, for example, the latest models for tackling crowd segmentation for the high-dimensional, large-scale anomaly detection, still encounter considerable uncertainties [23,24].…”

“…How to evaluate and compute the regulated attention in implications of the surrounding environment changes and, furthermore, how to define the warning level of EC-implications to video recognition efficiency should be research priorities towards precision security in intelligent surveillance [21][22][23][24][25][26][27]. It has been widely recognized that video surveillance should consider the implications of surrounding environments changes to video surveillance, but until now, there are still no models for a universal evaluation of EC-implications to video recognition efficiency [4,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. To solve the unresolved issues associated with uncontrolled EC-implications, various novel optimization models were proposed and further applied in current learning systems [13][14][15].…”

Real-time hardware–software embedded vision system for ITS smart camera implemented in Zynq SoC

“…In some cases, it is necessary to temporary cache the full image frame. Examples include two-pass connected component labelling [27], optical flow computation [31] or foreground object segmentation [34]. Nevertheless, the data temporarily stored in the buffer are transmitted again to the rest of the system in the form of a video stream.…”