Alexander Filonenko

We propose a method for fire flame detection that is suitable for modern surveillance systems. The algorithm performs automatic detection of flames and does not require adjusting its parameters. All color and shape features are calculated using integral images allowing changing search windows without increasing processing time drastically. The processing time is kept low via a combination of parallel processing on cores of the central processing unit and graphics processing unit. Detection does not rely on background subtraction; thus, it can resist camera jitter and rotation. The method was tested on a standard fire dataset. Experimental results confirm the reliability of the method and fast processing of 1920×1080 video sequences at 10 fps.

Smoke detection is a key component of disaster and accident detection. Despite the wide variety of smoke detection methods and sensors that have been proposed, none has been able to maintain a high frame rate while improving detection performance. In this paper, a smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information. The method takes advantage of the use of a stationary camera by using a background subtraction method to detect changes in the scene. The color of the smoke is used to assess the probability that pixels in the scene belong to a smoke region. Due to the variable density of the smoke, not all pixels of the actual smoke area appear in the foreground mask. These separate pixels are united by morphological operations and connected-component labeling methods. The existence of a smoke region is confirmed by analyzing the roughness of its boundary. The final step of the algorithm is to check the density of edge pixels within a region. Comparison of objects in the current and previous frames is conducted to distinguish fluid smoke regions from rigid moving objects. Some parts of the algorithm were boosted by means of parallel processing using CUDA GPUs, thereby enabling fast processing of both low-resolution and high-definition videos. The algorithm was tested on multiple video sequences and demonstrated appropriate processing times for a realistic range of frame sizes.

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance.

This paper presents development of vision-based intelligent surveillance system which covers multiple tasks, such as abandoned object detection, fire and smoke detection, human detection and tracking, sterile zone monitoring, and illegally parked vehicle detection. Our goal is combining all these tasks into integrated system considering not only high accuracy, but also fast processing time.

Image-based surveillance systems are widely employed toward safety and security applications in many fields. Cameras, that are connected over an IP network for monitoring public areas, can produce large quantities of video footage. It is tedious for humans to simultaneously observe every type of event on several cameras. Thus, it is necessary to build a userfriendly intelligent system, enabling the analysis of video to detect suspicious events. One of the most important tasks of this system would be to identify unattended objects to prevent an unexpected accident such as the bombing of a public space. This paper presents a novel technique for such a task. The method is based on a sequence of dual background differences, which is obtained by computing the intensity difference between the current and reference background models within a time period. A clustering and object detector are then integrated to identify the unattended objects. The effectiveness of method was verified using public and our own databases. The results confirmed that the method is efficient to detect unattended objects and is suitable for implementation in video surveillance systems.

As one of the traffic monitoring tasks, detecting an illegally parked vehicle aims to prevent car crashing between parked and other vehicles. However, developing such a task becomes more complex due to weather conditions, occlusion, illumination changing, and other factors. This work addresses a framework to detect an illegally parked vehicle using a cumulative dual foreground difference. In our framework, two background models with different learning rates are generated based on a Gaussian mixture model, defined as short- and long-term models. Each model extracts foreground pixels and the stability of these pixels are then analyzed based on cumulative values and temporal positions over a certain period of time. Subsequently, the connected component labeling is performed on the static pixels to form stable regions. To determine whether the candidate region is vehicle, a rule-based filtering approach is performed. Finally, the detection-based tracking is applied to reduce false positives. The effectiveness of the proposed framework is evaluated using i-LIDS and ISLab dataset. The experiment results show that the proposed framework is efficient and robust to detect an illegally parked vehicle. Thus, it can be considered as one of the task solutions for a traffic monitoring system.