Due to the need for increased security measures for monitoring and safeguarding the activities, video anomaly detection is considered as one of the significant research aspects in the domain of computer vision. Assigning human personnel to continuously check the surveillance videos for finding suspicious activities such as violence, robbery, wrong U‐turns, to mention a few, is a laborious and error‐prone task. It gives rise to the need for devising automated video surveillance systems ensuring security. Motivated by the same, this paper addresses the problem of detection and localization of anomalies from surveillance videos using pipelined deep autoencoders and one‐class learning. Specifically, we used a convolutional autoencoder and a sequence‐to‐sequence long short‐term memory autoencoder in a pipelined fashion for spatial and temporal learning of the videos, respectively. The authors followed the principle of one‐class classification for training the model on normal data and testing it on anomalous testing data. The authors achieved a reasonably significant performance in terms of an equal error rate and the time required for anomaly detection and localization comparable to standard benchmarked approaches, thus, qualifies to work in a near‐real‐time manner for anomaly detection and localization.

Due to wide availability of programmable, off-the-shelf sensors and advancements in Radio Frequency Identification Technologies as well as Wireless Sensor Networks, the Internet of Things (IoT) is making our lives smarter than ever before. The abundant growth in sensors triggers the spontaneous generation of IoT data at colossal scale which is referred to as big data. Processing, analyzing this data in a secure, timely and efficient manner has become the sine qua non for the successful working of IoT paradigm and thereby emanating the need of Data Analytic Platforms in IoT based smart systems. The data analytic platform performs analytics over massive data and provides crucial baseline for decision and planning in the IoT sphere. The fully matured cloud computing technology best fit the scenario of IoT and big data analytics offering powerful resources to carry out analytical and management tasks. The paper focuses on various big data analytic platforms for IoT in cloud computing domain.