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.
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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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Journal Article•
TL;DR: These notes describe the derivation of a simple algorithm for signal denoising (filtering) based on total variation (TV), based on the min-max property and the majorization-minimization procedure.
Abstract: These notes describe the derivation of a simple algorithm for signal denoising (filtering) based on total variation (TV). Total variation based filtering was introduced by Rudin, Osher, and Fatemi [8]. TV denoising is an effective filtering method for recovering piecewise-constant signals. Many algorithms have been proposed to implement total variation filtering. The one described in these notes is by Chambolle [3]. (Note: Chambolle described another algorithm in [2]). Although the algorithm can be derived in several different ways, the derivation presented here is based on descriptions given in [1, 10]. The derivation is based on the min-max property and the majorization-minimization procedure. Total variation is often used for image filtering and restoration, however, to simplify the presentation of the TV filtering algorithm these notes concentrate on one-dimensional signal filtering only. In addition, the algorithm described here may converge slowly for some problems. Faster algorithms for TV filtering have recently been developed, for example [1,10]. The development of fast, robust algorithms for TV and related non-linear filtering is an active topic of research.
45 citations
"Design of smart video surveillance ..." refers methods in this paper
...2 using majorization minimization algorithm [14] at pixel location to get the background video L....
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TL;DR: A unified framework for generating a single high-quality still image ("snapshot”) from a short video clip that provides a visual summary of activity in the video by incorporating saliency-based objectives in the snapshot formation process.
Abstract: We describe a unified framework for generating a single high-quality still image ("snapshot”) from a short video clip. Our system allows the user to specify the desired operations for creating the output image, such as super resolution, noise and blur reduction, and selection of best focus. It also provides a visual summary of activity in the video by incorporating saliency-based objectives in the snapshot formation process. We show examples on a number of different video clips to illustrate the utility and flexibility of our system.
42 citations
"Design of smart video surveillance ..." refers background or methods in this paper
...We tested the summarization algorithm for different input videos [21]....
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...Park video [22] (a) Salient Stills [23] (b) Video Synopsis [22] (c) Video Snapshot [21] (d) Perceptual Summary....
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TL;DR: This paper introduces for the first time the features of the human visual system within the summarization framework itself to allow for the emphasis of perceptually significant events while simultaneously eliminating perceptual redundancy from the summaries.
Abstract: The enormous growth of video content in recent times has raised the need to abbreviate the content for human consumption. Thus, there is a need for summaries of a quality that meets the requirements of human users. This also means that the summarization must incorporate the peculiar features of human perception. We present a new framework for video summarization in this paper. Unlike many available summarization algorithms that utilize only statistical redundancy, we introduce for the first time the features of the human visual system within the summarization framework itself to allow for the emphasis of perceptually significant events while simultaneously eliminating perceptual redundancy from the summaries. The subjective and objective evaluation scores have evaluated the framework.
38 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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IBM1
TL;DR: The IBM smart surveillance system 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: One of the key components of tele-presence systems is automatic awareness of the remote environment. This very same capability of automatic situation awareness is currently being developed and deployed in the context of the next generation smart surveillance systems. Smart surveillance systems use a number of automatic video analysis techniques like object detection, tracking and classification in conjunction with database and web application servers to provide users with the capability of distributed smart surveillance. The IBM smart surveillance system 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.
36 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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19 Aug 2016
TL;DR: This paper proposes a novel dehazing method to increase visibility from a single view without using any prior knowledge about the outdoor scene and uses stochastic iterative algorithm to remove fog and haze.
Abstract: Images captured in presence of fog, haze or snow usually suffer from poor contrast and visibility. In this paper we propose a novel dehazing method to increase visibility from a single view without using any prior knowledge about the outdoor scene. The proposed method estimates a visibility map of the scene from the input image and uses stochastic iterative algorithm to remove fog and haze. The method can be applied to color and grayscale images. Experimental results show that the proposed algorithm outperforms most of the state-of-the-art algorithms in terms of contrast, colorfulness and visibility.
15 citations
"Design of smart video surveillance ..." refers background or methods or result in this paper
...We have used images from database [20] to evaluate the performance of the proposed visibility enhancement algorithm....
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...Comparison of Various Image Enhancement Methods; Column (a) contains test images from database [20], Columns (b), (c), and (d) show enhanced results for Berman [19], Bhattacharya [20], and proposed method respectively...
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...Image Size Bhattacharya [20] (s) Berman [19] (s) Proposed (s) 640 x 480 48 4....
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...compared to state of the art techniques [19], [20]....
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