Design of smart video surveillance system for indoor and outdoor scenes
01 Aug 2017-pp 1-5
TL;DR: A novel surveillance system that enhances visibility in adverse weather conditions and summarizes the captured videos automatically to reduce storage space is proposed and perceptual features that can be used for more meaningful and robust summarization of the video than the existing summarization algorithms are proposed.
Abstract: Smart video surveillance of indoor and outdoor scenes is a challenging task for modern surveillance systems. Different imaging conditions like bad illumination, adverse weather, etc., makes the surveillance process difficult. Recently, researchers have proposed smart surveillance systems with additional features for more accurate monitoring of events, but not much attention is paid to improve the system such that the monitoring process consumes as minimum resources as possible. In this paper, we propose a novel surveillance system that enhances visibility in adverse weather conditions and summarizes the captured videos automatically to reduce storage space. As the summarization process is based on the events in a scene, video interpretation becomes fast and easy. We propose perceptual features that can be used for more meaningful and robust summarization of the video than the existing summarization algorithms. We test the system for both indoor and outdoor scenes and show that the system works well even with multiple moving objects and complex motions.
Citations
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01 Oct 2018
TL;DR: The design and development of an embedded system for intelligent video surveillance with IoT capabilities is presented and an OMRON biometric sensor with specific features for face, body and hand detection was used.
Abstract: Video Surveillance systems are widely used in indoor and outdoor environments for prevention and security monitoring. Most of conventional video surveillance systems are designed to store huge amount of data which difficult efficient access to the data from remote locations due to bandwidth requirements. A smart surveillance system allows efficient data storage and flexible data access. In this document the design and development of an embedded system for intelligent video surveillance with IoT capabilities is presented. For this project, an OMRON biometric sensor with specific features for face, body and hand detection was used. Face detection provides a criterion for event detection and efficient data capture of the data. The information of interest can be retrieved from a smartphone through Telegram X app. The system was tested under different face conditions including variations of pose, partial occlusion and expression. The system was developed with specific and smart devices providing new and different designs, easily to connect and control for users, without forgetting the importance of security.
8 citations
Cites background from "Design of smart video surveillance ..."
...[6] presentan un modelo unificado para monitoreo y síntesis de datos correspondientes a una secuencia de video....
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TL;DR: The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation and produces reliable dehazed output in varying haze conditions, unlike current methods.
Abstract: Dehazing is an important process as it can significantly improve the performance of computer vision applications in outdoor environments. The two main requirements that an online dehazing system demands are low processing time and high visual range. The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation. Estimation of atmospheric scattering parameter and transmission map forms the key step in dehazing problem. At first, the authors use CUP to generate the transmission map and refine it further by Fast Guided Filter. They estimate the atmospheric scattering parameter with the help of the estimated transmission map. Experimental results show that the quality of dehazed output, produced in real-time using the proposed method, is comparable with the results achieved by the state of the art techniques. The proposed dehazing method produces reliable dehazed output in varying haze conditions, unlike current methods.
4 citations
01 Feb 2020
TL;DR: The proposed solution aims at selecting keyframes from the video based on two criteria i.e. each object should appear within the scope of frame and each object must be visually presentable and must be closer to each other so that it could only show the related activities for ex.
Abstract: Today, System comprised of Surveillance cameras has become very useful and important in the every field, Mostly in the security industry. Also, Many numbers of surveillance cameras get added to the networks of surveillance or system every year as need and importance of surveillance cameras is increasing day by day. Video recorded from these surveillance cameras are large in size which require huge amount of time for monitoring and large storage space. Hence, there is a need of video summarization which has become very prominent since the last ten years because of the huge amount of available digital video content [3]. An algorithm we used for video summarization typically takes surveillance video as an input and extract a set of important frames or key-frames which is useful to represent the entire video content which are effectively more concise as compared to the original input video and convey semantic meaning. So, Our proposed solution aims at selecting keyframes from the video based on two criteria i.e. each object should appear within the scope of frame and each object should be visually presentable and must be closer to each other so that it could only show the related activities for ex. Summarization of video captured from ATM room camera should only display the part where user is interacting with the machine. So such a key frames are then used in final summarization.
4 citations
Cites background from "Design of smart video surveillance ..."
...Surveillance system basically comprises of such cameras which are placed at public and private premises and are capable to capture videos that can be stored and sent over communication network [7]....
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01 Oct 2019
TL;DR: This paper presents the performance evaluation of a metadata database (DB) management method that uses realistic numeric examples for IoT Live Data and assumes that the metadata of Live Data with high usefulness for sharing by many users/services would dominate all metadata.
Abstract: This paper presents the performance evaluation of a metadata database (DB) management method that uses realistic numeric examples for IoT Live Data. The method is proposed to reduce the handling costs of Live Data. Live Data are here defined as data that are typically continuously generated by IoT devices and have short lifetimes (e.g., 10 fps surveillance camera images). We have already proposed an evaluation model in which the high locality is significantly featured in Live Data usage. The previous evaluation results are obtained only from general parameter values in statistical distributions. To evaluate realistic situations, this paper assumes that the metadata of Live Data with high usefulness for sharing by many users/services would dominate all metadata. In particular, for such data, we use both surveillance camera images and social networking service contents. The median values and the expected values are set considering the surveillance camera's locality (defined as the average distance between a surveillance camera and the users of its camera images). As a result, the proposed method can reduce the DB update costs by 99.0% while the additional search costs are reduced by up to 27.8% compared with the conventional metadata management method. The additional search costs are negligible compared with the reduction in DB update costs, since the number of searches is much smaller than the number of DB updates with respect to the number of update/search epochs.
4 citations
Cites background from "Design of smart video surveillance ..."
...Surveillance camera images have a wide range of services that can be utilized by image processing [12,13] so that they are highly useful for sharing....
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05 Sep 2020
TL;DR: A thorough study of making of an efficient surveillance system along with a feature of automatically informing the owner about the suspicious movement, finding that faster RCNN is much accurate than the other conventional methods.
Abstract: The present document represents a thorough study of the making of an efficient surveillance system along with a feature of automatically informing the owner about the suspicious movement. In this moving world, normally people are suffering from the availability of time, so if any crime has happened at the site, it will take many days of searching for finding the actual presence of criminals, and thus a good chance for those burglars to flee away to protect themselves. For making the task possible, chose Python as the weapon for this battle and used different efficient techniques like COCO dataset for getting labeled and annotated images, LabelImg for making the annotation set of images, TensorFlow, object detection API for object detection and faster RCNN for training as faster RCNN has shown the highest accuracy for the COCO dataset so far. The owner can be informed in two ways: Either send a message to him via mail or phone or call at the time of suspicious image capturing. Here, both of these cases are used: For mail, the task is done via SMTP and for phone calls Twilio is used which provides us registered phone no. and can make both outbound and inbound calls. After using all the mentioned things and making the model in a way described above, it was found that faster RCNN is much more accurate than the other conventional methods. The results have been very well as RCNN show 86.7% accuracy and 100% has come out with the informing module as there simply the mail will be sent to the one whose mail is given in the code and the same is for Twilio calling.
2 citations
Cites background from "Design of smart video surveillance ..."
...For the same, researchers have proposed many algorithms as in [1] transmittance algorithm and enhancement algorithm for the visual enhancement and visibility range algorithm for pre processing and decomposition algorithm for doing background separation but the system will detect and save the images with it....
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References
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24 Mar 2014
TL;DR: This work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicate through a shared memory.
Abstract: Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions, required to allow the execution of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities performed by humans in the scene, so that the security personnel can pay closer attention to these preselected activities. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking and re-identification, face recognition, and action recognition. Even though each of these problems have been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input. Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicate through a shared memory.
15 citations
TL;DR: A new saliency prediction model that accounts for different pixel-level attributes as color, contrast and intensity; object level attributes such as size, shape of objects and semantic level attributes as motion and speed of objects is described.
12 citations
"Design of smart video surveillance ..." refers methods in this paper
...We introduce motion contrast, motion energy, and motion chromism as the features [16], [17] to select the key frames....
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TL;DR: This work proposes a novel algorithm which detects the motion salient regions by decomposing the input video into background and residual videos in much lesser time without sacrificing the accuracy of the decomposition.
Abstract: As human vision system is highly sensitive to motion present in a scene, motion saliency forms an important feature in a video sequence. Motion information is used for video compression, object segmentation, object tracking and in many other applications. Though its applications are extensive, accurate detection of motion in a given video is complex and computationally expensive for the solutions reported in the literature. Decomposing a video into visually similar and residual videos is a robust way to detect motion salient regions. The existing decomposition techniques require large execution time as the standard form of the problem is NP-hard. We propose a novel algorithm which detects the motion salient regions by decomposing the input video into background and residual videos in much lesser time without sacrificing the accuracy of the decomposition. In addition, the proposed algorithm is completely parallelizable that ensures further reduction in computational time with the use of advanced multicore processors.
11 citations
"Design of smart video surveillance ..." refers background or methods in this paper
...We perform the background subtraction process using parallelizable video decomposition algorithm [12], [13]....
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...where bp is the estimated background intensity at pixel location p along time axis and M is a difference matrix of dimension (K−1)×K that computes the difference between two consecutive elements of estimated vector bp [13]....
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01 Aug 2017
TL;DR: The proposed method uses color uniformity principle to detect hole regions present in depth map and provides a framework to identify falsely detected holes in order to increase effectiveness of the method.
Abstract: Depth map estimation forms an integral part of many applications such as 2D-to-3D creation. There exists various methods in literature for depth map estimation using different cues and structure. Usually, depth information is decoded from these cues at the edges and matting is applied to spread it over neighboring regions. Defocus is one such cue due to its natural existence and does not require any precondition compared to other cues. However, there can exist regions in images with no edges. These regions are referred to hole regions and are the main source of error in estimated depth map. In this paper, we propose a method to correct some of these errors to obtain an accurate depth map. The proposed method uses color uniformity principle to detect hole regions present in depth map. We also provide a framework to identify falsely detected holes in order to increase effectiveness of our method.
7 citations
"Design of smart video surveillance ..." refers methods in this paper
...We estimate the transmittance map t of every frame using soft texture characterization from color uniformity principle [9] and refine it with fast guided filter [10]....
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21 Jul 2015
TL;DR: This work proposes a novel total variation based decomposition method which is an order of magnitude faster than the existing methods and completely parallelizable and a novel method for reconstructing old color films affected with partial color artifact (PCA) and blotches using the decomposition technique.
Abstract: Video decomposition into visually similar part and feature part has gained considerable importance due to its applications in different fields of video processing. Some of the diverse problems that can be handled using this decomposition technique are- background estimation, motion saliency detection, single object tracking, multiple object tracking, artifact detection, compression and various others. Though for the technical integrity, video decomposition becomes an obvious tool for video processing, the present approaches provide solutions which are computationally expensive and non-parallelizable. We propose a novel total variation based decomposition method which is an order of magnitude faster than the existing methods and completely parallelizable. We also propose a novel method for reconstructing old color films affected with partial color artifact (PCA) and blotches using the decomposition technique.
6 citations
"Design of smart video surveillance ..." refers methods in this paper
...We perform the background subtraction process using parallelizable video decomposition algorithm [12], [13]....
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