Now-a-days, security and surveillance has become an integral part of our everyday life. There is a need for an Intelligent Surveillance systems for the being developed Smart Cities to ensure safety at all levels. The Objective of this paper is to demonstrate an Integrated framework for Vehicle detection and classification from real-time video captured from the road traffic. This work proposed a complete framework for Surveillance System for Indian smart cities with an aim to improve the security and surveillance of vehicles in varying weather conditions. To realize road traffic flow surveillance under various environments which contain poor visibility conditions, this paper provides a solution to extract the required information from surveillance video under different weather condition like day, night and rain. Also proposed system will dynamically choose the respective algorithm based on identified nature of the weather. In Vehicle count and classification, algorithm which is used based on image segmentation using a Laplacian of Gaussian edge detector (LoG), morphological filtering of the edge map objects and classification into small, medium and large vehicles on the basis of size using a nearest centroid minimum distance classifier. The proposed approach can be used for both stationary and fast moving traffic in contrast to motion detection based approaches. The algorithm was implemented in Python and average detection and classification accuracies of 96.0% and 89.4% respectively were achieved for fast moving traffic, while for slow moving traffic, 82.1% and 83.8% respectively were achieved.

如今，安全和监视已成为我们日常生活不可或缺的一部分。需要用于正在开发的智能城市的智能监视系统，以确保各个级别的安全。本文的目的是演示一种用于从道路交通中捕获的实时视频进行车辆检测和分类的集成框架。这项工作为印度智能城市提出了一个完整的监控系统框架，旨在提高在不同天气条件下车辆的安全性和监视能力。为了在可见性差的各种环境下实现道路交通流量监控，本文提供了一种从白天，黑夜，雨天等不同天气条件下的监控视频中提取所需信息的解决方案。还提出的系统将基于所识别的天气性质动态地选择相应的算法。在“车辆计数和分类”中，基于高斯边缘检测器（LoG）的拉普拉斯算子对图像进行分割，对边缘地图对象进行形态过滤并根据尺寸使用最近的质心将其分类为小型，中型和大型车辆的算法最小距离分类器。与基于运动检测的方法相比，该方法可用于固定交通和快速交通。该算法在Python中实现，快速移动流量的平均检测和分类准确率分别为96.0％和89.4％，而慢速移动流量的平均检测和分类准确率分别为82.1％和83.8％。一种算法，该算法基于使用高斯边缘检测器（LoG）的拉普拉斯算子进行图像分割，对边缘图对象进行形态过滤以及根据尺寸使用最近的质心最小距离分类器将其分类为小型，中型和大型车辆。与基于运动检测的方法相比，该方法可用于固定交通和快速交通。该算法在Python中实现，快速移动流量的平均检测和分类准确率分别为96.0％和89.4％，而慢速移动流量的平均检测和分类准确率分别为82.1％和83.8％。一种算法，该算法基于使用高斯边缘检测器（LoG）的拉普拉斯算子进行图像分割，对边缘图对象进行形态过滤以及根据尺寸使用最近的质心最小距离分类器将其分类为小型，中型和大型车辆。与基于运动检测的方法相比，所提出的方法可用于固定流量和快速移动流量。该算法在Python中实现，快速移动流量的平均检测和分类准确率分别为96.0％和89.4％，而慢速移动流量的平均检测和分类准确率分别为82.1％和83.8％。中型和大型车辆根据尺寸使用最近的质心最小距离分类器。与基于运动检测的方法相比，该方法可用于固定交通和快速交通。该算法在Python中实现，快速移动流量的平均检测和分类准确率分别为96.0％和89.4％，而慢速移动流量的平均检测和分类准确率分别为82.1％和83.8％。中型和大型车辆根据尺寸使用最近的质心最小距离分类器。与基于运动检测的方法相比，该方法可用于固定交通和快速交通。该算法在Python中实现，快速移动流量的平均检测和分类准确率分别为96.0％和89.4％，而慢速移动流量的平均检测和分类准确率分别为82.1％和83.8％。