Intelligent Transportation Systems (ITS) have become an important pillar in modern “smart city” framework which demands intelligent involvement of machines. Traffic load recognition can be categorized as an important and challenging issue for such systems. Recently, Convolutional Neural Network (CNN) models have drawn considerable amount of interest in many areas such as weather classification, human rights violation detection through images, due to its accurate prediction capabilities. This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN-SOPC, which uses an intelligent retraining mechanism of the CNN with known environments. The proposed methodology is capable of enhancing the efficacy of the approach by 2.44x in comparison to the state-of-art and proven through experimental analysis. We have also introduced a mathematical equation, which is capable of quantifying the suitability of using different CNN models over the other for a particular application based implementation.
The first machine learning method for ground truthing a video is proposed.We devise different sampling strategies for training different categories.Our results can be considered within the error margin of a human. With the increasing number of machine learning methods used for segmenting images and analyzing videos, there has been a growing need for large datasets with pixel accurate ground truth. In this letter, we propose a highly accurate semi-automatic method for segmenting foreground moving objects pictured in surveillance videos. Given a limited number of user interventions, the goal of the method is to provide results sufficiently accurate to be used as ground truth. In this paper, we show that by manually outlining a small number of moving objects, we can get our model to learn the appearance of the background and the foreground moving objects. Since the background and foreground moving objects are highly redundant from one image to another (videos come from surveillance cameras) the model does not need a large number of examples to accurately fit the data. Our end-to-end model is based on a multi-resolution convolutional neural network (CNN) with a cascaded architecture. Tests performed on the largest publicly-available video dataset with pixel accurate groundtruth (changdetection.net) reveal that on videos from 11 categories, our approach has an average F-measure of 0.95 which is within the error margin of a human being. With our model, the amount of manual work for ground truthing a video gets reduced by a factor of up to 40. Code is made publicly available at: https: github.com zhimingluo MovingObjectSegmentation