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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TL;DR: A new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines is described.
Abstract: Photographs of hazy scenes typically suffer having low contrast and offer a limited visibility of the scene. This article describes a new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines. We derive a local formation model that explains the color-lines in the context of hazy scenes and use it for recovering the scene transmission based on the lines' offset from the origin. The lack of a dominant color-line inside a patch or its lack of consistency with the formation model allows us to identify and avoid false predictions. Thus, unlike existing approaches that follow their assumptions across the entire image, our algorithm validates its hypotheses and obtains more reliable estimates where possible. In addition, we describe a Markov random field model dedicated to producing complete and regularized transmission maps given noisy and scattered estimates. Unlike traditional field models that consist of local coupling, the new model is augmented with long-range connections between pixels of similar attributes. These connections allow our algorithm to properly resolve the transmission in isolated regions where nearby pixels do not offer relevant information. An extensive evaluation of our method over different types of images and its comparison to state-of-the-art methods over established benchmark images show a consistent improvement in the accuracy of the estimated scene transmission and recovered haze-free radiances.
842 citations
"Design of smart video surveillance ..." refers methods in this paper
...Whereas, state-of-the-art methods [8], [18], and [19] results in Ē and...
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...For a quantitative comparison of the enhanced algorithm, we used the metrics entropy E and ratio of visible edges after and before enhancement Qe with state-of-the-art techniques [8], [18], and [19] on standard test images from [18]....
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TL;DR: Video synopsis provides a short video representation, while preserving the essential activities of the original video, in order to create a synopsis of an endless video streams, as generated by Webcams and by surveillance cameras.
Abstract: The amount of captured video is growing with the increased numbers of video cameras, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, most captured video is never watched or examined. Video synopsis is an effective tool for browsing and indexing of such a video. It provides a short video representation, while preserving the essential activities of the original video. The activity in the video is condensed into a shorter period by simultaneously showing multiple activities, even when they originally occurred at different times. The synopsis video is also an index into the original video by pointing to the original time of each activity. Video synopsis can be applied to create a synopsis of an endless video streams, as generated by Webcams and by surveillance cameras. It can address queries like "show in one minute the synopsis of this camera broadcast during the past day''. This process includes two major phases: (i) an online conversion of the endless video stream into a database of objects and activities (rather than frames). (ii) A response phase, generating the video synopsis as a response to the user's query.
331 citations
"Design of smart video surveillance ..." refers background in this paper
...Park video [22] (a) Salient Stills [23] (b) Video Synopsis [22] (c) Video Snapshot [21] (d) Perceptual Summary....
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Posted Content•
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TL;DR: The guided filter can be simply sped up from O(N) time to O( N/s^2) time for a subsampling ratio s, leading to a speedup of >10x with almost no visible degradation.
Abstract: The guided filter is a technique for edge-aware image filtering. Because of its nice visual quality, fast speed, and ease of implementation, the guided filter has witnessed various applications in real products, such as image editing apps in phones and stereo reconstruction, and has been included in official MATLAB and OpenCV. In this note, we remind that the guided filter can be simply sped up from O(N) time to O(N/s^2) time for a subsampling ratio s. In a variety of applications, this leads to a speedup of >10x with almost no visible degradation. We hope this acceleration will improve performance of current applications and further popularize this filter. Code is released.
163 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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IBM1
TL;DR: The IBM smart surveillance system (S3) is one of the few advanced surveillance systems which provides not only the capability to automatically monitor a scene but also the ability to manage the surveillance data, perform event based retrieval, receive real time event alerts thru standard web infrastructure and extract long term statistical patterns of activity.
Abstract: The increasing need for sophisticated surveillance systems and the move to a digital infrastructure has transformed surveillance into a large scale data analysis and management challenge. Smart surveillance systems use automatic image understanding techniques to extract information from the surveillance data. While the majority of the research and commercial systems have focused on the information extraction aspect of the challenge, very few systems have explored the use of extracted information in the search, retrieval, data management and investigation context. The IBM smart surveillance system (S3) is one of the few advanced surveillance systems which provides not only the capability to automatically monitor a scene but also the capability to manage the surveillance data, perform event based retrieval, receive real time event alerts thru standard web infrastructure and extract long term statistical patterns of activity. The IBM S3 is easily customized to fit the requirements of different applications by using an open-standards based architecture for surveillance.
138 citations
"Design of smart video surveillance ..." refers background in this paper
...Some pioneering works proposed by researchers of IBM are reported in [1], [2], [3], [4]....
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IBM1
TL;DR: This paper starts with a threat model for airports and uses this to derive the security requirements to motivate an open-standards based architecture for surveillance, and discusses the critical aspects of this architecture and its implementation in the IBM S3 smart surveillance system.
Abstract: As smart surveillance technology becomes a critical component in security infrastructures, the system architecture assumes a critical importance. This paper considers the example of smart surveillance in an airport environment. We start with a threat model for airports and use this to derive the security requirements. These requirements are used to motivate an open-standards based architecture for surveillance. We discuss the critical aspects of this architecture and its implementation in the IBM S3 smart surveillance system. Demo results from a pilot deployment in Hawthorne, NY are presented.
115 citations
"Design of smart video surveillance ..." refers background in this paper
...The smart surveillance system proposed in [1] has additional analytical steps to annotate a video to detect several features, e....
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...Some pioneering works proposed by researchers of IBM are reported in [1], [2], [3], [4]....
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