Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.

This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.

Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of “smart city.” Video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of real-time media data are transmitted to and processed in the cloud. However, the cloud relies on network connectivity of the Internet that is sometimes limited or unavailable; thus, the centralized framework is not sufficient for real-time processing of media data needed for smart video surveillance. To tackle this problem, edge computing - a technique for accelerating the development of AIoT (AI across IoT) in smart cities - can be conducted. In this paper, a distributed real-time object detection framework based on edge-cloud collaboration for smart video surveillance is proposed. When collaborating with the cloud, edge computing can serve as converged computing through which media data from distributed edge devices of the network are consolidated by AI in the cloud. After AI discovers global knowledge in the cloud, it to be shared at the edge is deployed remotely on distributed edge devices for real-time smart video surveillance. First, the proposed framework and its preliminary implementation are described. Then, the performance evaluation is provided regarding potential benefits, real-time responsiveness and low-throughput media data transmission.

The key advantage of using power-utility-owned smart meters is the ability to transmit electrical energy consumption data to power utilities’ remote data centers for various purposes, such as billing. Several useful consumer-centric use cases can also be identified for the collection and further analysis of consumers’ electrical energy consumption data from smart meters. One of the use cases is home automation. Recent related solutions for home automation involving home security and healthcare depend on the installation of sensors and/or other devices such as video cameras, which have high costs for installation and annual maintenance. Because the electrical energy consumption patterns mined from smart meter data are indicative of residents’ daily life, it is possible to develop a new home automation approach based on energy decomposition for smart home automation. Accordingly, in this work, a smart home energy management system (SHEMS) utilizing a parallel-processing-implemented, GPU-accelerated neurocomputing-based time-series load modeling and forecasting mechanism is proposed for smart home automation. Energy decomposition is used to facilitate the time-series load modeling and forecasting mechanism, which tracks appliance-level electrical energy consumption to be quantitatively modeled from circuit-level consumption, with no intrusive deployment of networked plug-level power meters for individual electrical home appliances. For the neurocomputing approach applied in this mechanism, an autoregressive multilayer perceptron methodology is compared against a stacked long short-term memory methodology. The presented neurocomputing-based time-series load modeling and forecasting mechanism facilitated by energy decomposition is capable of predicting residents’ daily behavioral patterns by nonintrusively analyzing and modeling relevant electrical home appliances based on their past trends for smart home automation.

Memory requirements (for storing intermediate signals) and critical path are essential issues for 2-D (or multidimensional) transforms. This paper presents new algorithms and hardware architectures to address the above issues in 2-D dual-mode (supporting 5/3 lossless and 9/7 lossy coding) lifting-based discrete wavelet transform (LDWT). The proposed 2-D dual-mode LDWT architecture has the merits of low transpose memory (TM), low latency, and regular signal flow, making it suitable for very large-scale integration implementation. The TM requirement of the $N\times N$ 2-D 5/3 mode LDWT and 2-D 9/7 mode LDWT are $2N$ and $4N$, respectively. Comparison results indicate that the proposed hardware architecture has a lower lifting-based low TM size requirement than the previous architectures. As a result, it can be applied to real-time visual operations such as JPEG2000, motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding applications.