Cloud-based scalable object detection and classification in video streams

Due to the recent advances in cameras, cell phones and camcorders, particularly the resolution at which they can record an image/video, large amounts of data are generated daily. This video data is often so large that manually inspecting it for useful content can be time consuming and error prone, thereby it requires automated analysis to extract useful information and metadata. Existing video analysis systems lack automation, scalability and operate under a supervised learning domain, requiring substantial amounts of labelled data and training time. We present a cloud-based, automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number and size of the stream(s) being considered. The system automates the video analysis process and reduces manual intervention. An operator using this system only specifies which object of interest is to be located from the video streams. Video streams are then automatically fetched from the cloud storage and analysed in an unsupervised way. The proposed system was able to locate and classify an object of interest from one month of recorded video streams comprising 175 GB in size on a 15 node cloud in 6.52 h. The GPU powered infrastructure took 3 h to accomplish the same task. Occupancy of GPU resources in cloud is optimized and data transfer between CPU and GPU is minimized to achieve high performance. The scalability of the system is demonstrated along with a classification accuracy of 95%

Traffic monitoring using video analytics in clouds

Traffic monitoring is a challenging task on crowded roads. Traditional traffic monitoring procedures are manual, expensive, time consuming and involve human operators. They are subjective due to the very involvement of human factor and sometimes provide inaccurate/incomplete monitoring results. Large scale storage and analysis of video streams were not possible due to limited availability of storage and compute resources in the past. Recent advances in data storage, processing and communications have made it possible to store and process huge volumes of video data and develop applications that are neither subjective nor limited in feature sets. It is now possible to implement object detection and tracking, behavioural analysis of traffic patterns, number plate recognition and automate security and surveillance on video streams produced by traffic monitoring and surveillance cameras. In this paper, we present a video stream acquisition, processing and analytics framework in the clouds to address some of the traffic monitoring challenges mentioned above. This framework provides an end-to-end solution for video stream capture, storage and analysis using a cloud based GPU cluster. The framework empowers traffic control room operators by automating the process of vehicle identification and finding events of interest from the recorded video streams. An operator only specifies the analysis criteria and the duration of video streams to analyse. The video streams are then automatically fetched from the cloud storage, decoded and analysed on a Hadoop based GPU cluster without operator intervention in our framework. It reduces the latencies in video analysis process by porting its compute intensive parts to the GPU cluster. The framework is evaluated with one month of recorded video streams data on a cloud based GPU cluster. The results show a speedup of 14 times on a GPU and 4 times on a CPU when compared with one human operator analysing the same amount of video streams data

Modeling and analysis of a deep learning pipeline for cloud based video analytics.

Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. These systems necessitate massive amounts of labeled data and training time to perform fine tuning of hyper-parameters for object classification. We propose a cloud based video analytics system built upon an optimally tuned deep learning model to classify objects from video streams. The tuning of the hyper-parameters including learning rate, momentum, activation function and optimization algorithm is optimized through a mathematical model for efficient analysis of video streams. The system is capable of enhancing its own training data by performing transformations including rotation, flip and skew on the input dataset making it more robust and self-adaptive. The use of in-memory distributed training mechanism rapidly incorporates large number of distinguishing features from the training dataset - enabling the system to perform object classification with least human assistance and external support. The validation of the system is performed by means of an object classification case-study using a dataset of 100GB in size comprising of 88,432 video frames on an 8 node cloud. The extensive experimentation reveals an accuracy and precision of 0.97 and 0.96 respectively after a training of 6.8 hours. The system is scalable, robust to classification errors and can be customized for any real-life situation.N/