In the decision making process the Data Analytics plays an important role. The Insights that are obtained from pattern analysis gives many benefits like cost cutting, good revenue, and better competitive advantage. On the other hand the patterns of frequent itemsets that are hidden consume more time for extraction when data increases over time. However less memory consumption is required for mining the patterns of frequent itemsets because of heavy computation. Therefore, an algorithm required must be efficient for mining the patterns of the frequent itemsets that are hidden which takes less memory with short run time. This paper presents a review of different algorithms for finding Frequent Patterns so that a more efficient algorithm for finding frequent items sets can be developed.
In a video surveillance system, tracking multiple moving objects using a single camera feed is having numerous challenges. A multi-camera system increases the output image quality in both overlapping and non-overlapping environment. Traffic behavior analysis is an intensified demand in a recent topic of research. Due to increasing traffic in intercity roads, interstate, and national highways. Automated traffic visual surveillance applications with the multi-camera are a topic of research in computer vision. This paper, present a multi-camera system study for the overlapping area of the road for traffic analysis in three sections. The second section represents the thorough literature survey on the multi-camera system. Here, the third section is our proposed system using a dual-camera experimental setup with their coordination. A deep neural network is used in the experiments for traffic behavior analysis. The emphasis of this paper is on the physical arrangement of the multi-camera system, calibration, and advantages- disadvantages. On a conclusion note, future development and advancement in traffic analysis using a multi-camera system is discussed.
Visual surveillance emerged as an active automated research area of Computer Vision from the traditional mathematical approach to neural networks. A novel modified neural network technique for object detection and classification for input images and video feed from many cameras overlapping target areas is presented in this research.Modified Neural Network methodology represents layered architecture as the input, preprocessing and Operation layer, to simplify the processing needed to prepare for training neural networks. This strategy aids in delegating the tasks to layers with predefined tasks thus simplifying training, reducing computational requirements, and delivering performance. Two modules of the Neural Network will process the input. The first module is a modified Neural Network and will differ from traditional Neural Network in respect of connectivity between Neurons and their operations. This will still be Neural Network for data shared and threshold followed for marking differences – Markers, between the two inputs and simplified training. The second Module will be a traditional Neural Network for detection and classification that will track the detected objects. This paper proposed a system that provides the combined image as an output from multiple cameras feed using an untraditional Mathematical and Algorithmic Approach.
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