Security has always been of paramount importance to humans. In the absence of a sense of security at one’s workplace, home or anywhere else, people feel uneasy and vulnerable. With the improvement of modern technology, along with the lack of time at hand, the need for faster, efficient, accurate as well as low-cost security techniques is more than ever. Image Captioning for Video Surveillance System is proposed to develop visual systems that generate contextual descriptions about objects in images, and then use these descriptions to provide information of the scene that needs to be secured. The proposed system uses a neural network model composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to caption the incoming video feed. The main significance of this paper is in integrating the system with Discrete Wavelet Transform (DWT), which is applied on the incoming video feed, so that the compressed LL band frames transferred wirelessly to the model are smaller in comparison, leading to less transfer time and faster processing by the model.
Security is always a main concern in every sphere, due to a rise in crime rate in a crowded event or suspicious lonely areas. Anomalydetection and observance have major applications of computer vision to gear various problems. Due to demand in the protection of safety, security of private properties placement of surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence observance. Detection of weapon and militant using convolution neural network (CNN). Proposed implementation uses two types of datasets. One dataset contains pre-labelled images. And the other one labelled manually contains a set of images. Results are tabulated, both algorithms achieve good efficiency, but their operation in real situations can be based on the trade-off between speed and efficiency. Crime is defined as an act dangerous not only to the person involved, but also to the community as a whole. It is to predict the crime using image dataset and finally calculate accurate performance of the detector. The propose algorithms that are able to alert the human operator when a weapon and militant is visible in the image. It is mainly focusedon limiting the number of false alarms in order to allow for real life application of the system. For future work, it is planned to use in live applicationand to improve the detection and reduce the crime.
Vision plays an important part which helps us to look at the world and perceive information about our surroundings. A human perceives information by looking at an object or the surrounding on the whole and tries to map visual features and attributes and by summarizing these features we can describe or tell about our surroundings. The way the human brain does this is still a huge mystery. But, For a machine/computer this task is what is called as Image Captioning. The computer or machine is fed with images from which they learn to extract features i.e pixel information, object position, geometry, etc. Using these features the machine tries to map it to a sentence word by word or on a whole which summarizes the information of the image. Due to the advancements in recent Computer Vision Methods and Deep Learning architectures, Computers have been able to correctly summarize images which have been fed to them. In this paper, we present a survey on the new types of architectures and the datasets which are being used to train such architectures. Furthermore, we have discussed future methods that can be implemented.
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