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Title: Multi-View Summarization and Activity Recognition Meet Edge Computing in IoT Environments

Paper content:
Multi-view video summarization (MVS) lacks researchers’ attention due to their major challenges of inter-view correlations and overlapping of cameras.
Most of the prior MVS works are offline, relying on only summary, needing extra communication bandwidth and transmission time with no focus on uncertain environments.
Different from the existing methods, we propose edge intelligence based MVS and spatio-temporal features based activity recognition for IoT environments.
We segment the multi-view videos on each slave device over edge into shots using light-weight CNN object detection model and compute mutual information among them to generate summary.
Our system does not rely on summary only but encode and transmit it to a master device with neural computing stick (NCS) for intelligently computing inter-view correlations and efficiently recognizing activities, thereby saving computation resources, communication bandwidth, and transmission time.
Experiments report an increase of 0.4 in F-measure score on MVS Office dataset as well as 0.2% and 2% increase in activity recognition accuracy over UCF-50 and YouTube 11 datasets, respectively, with lower storage and transmission time compared to state-of-the-art.
The time complexity is decreased from 1.23 to 0.45 secs for a single frame processing, thereby generating 0.75 secs faster MVS.
Furthermore, we made a new dataset by synthetically adding fog to an MVS dataset to show the adaptability of our system for both certain and uncertain surveillance environments.
Surveillance cameras installed indoor and outdoor at offices, public places, and roads generate huge amount of video data on daily basis.
This gigantic volume of data has two big issues: first one is storage consumption and second is huge computational complexity for its purposeful usage .
Video summarization aims at these problems by condensing the data size via extracting key information from lengthy videos and suppressing the redundant frames.
A video summary generated from a single camera is called single-view video summarization (SVS) (Mahasseni et al., 2017) .
On the other hand, a summary generated from a camera network is known as MVS (Panda et al., 2016a) .
SVS is intensively researched with applications to various domains including surveillance (Wang et al., 2017) , sports (Tejero-de Pablos et al., 2018) , and news videos (Wang et al., 2018) .
In contrast, MVS is not studied deeply because of several challenges such as computing inter-and intra-view correlations, overlapping regions among connected cameras, and variation in light conditions among different views.
The basic flow of MVS includes input acquisition, preprocessing, feature extraction, post-processing, and summary generation.
The mainstream MVS methods follow traditional machine learning approaches such as clustering along with low-level features extracted from entire frame with no focus on specific targets in surveillance.
The most important part of MVS is considering different objects in surveillance that can be useful for summary generation.
However, the existing techniques do not focus on objects such as persons and vehicles while generating summary.
Thus, the final summary may miss some important frames having persons or vehicles that need to be considered for MVS.
Furthermore, all the existing techniques rely only on MVS with no further steps for analysis of the generated summary.
For instance, the generated summary can be used for indexing, browsing, and activity recognition.
The existing methods are functional only in certain environments with no focus on uncertain scenarios (Min et al., 2019) , making them inadequate in real-world environments.
Finally, all the existing methods process data on local/online servers or personal computers with huge computation power.
It requires extra processing time, power of transmission, and does not guarantee quick responsive action for any abnormal situations, if not handled on the edge.
To ensure proper and quick responsive arrangements, activity recognition at edge is a necessary requirement of the current technological era.
Activity recognition literature is mature, but with no focus on processing over the edge.
Almost all the existing techniques classify activities over high computational local or cloud servers.
Classifying activity on edge is an important task of surveillance in smart cities.
Therefore, to tackle these challenges effectively, we present a novel framework applicable in both certain and uncertain environments for MVS and activity recognition over the edge.
Figure 1: Input and output flow of our proposed framework.
(a) Video frames (both certain and uncertain environment) from resource constrained devices.
(b) Annotate frames by detecting objects of interest, apply keyframes selection mechanism, generate summary, encode and transmit it to master device.
(c) Decode generated summary, perform features extraction, and forward it to activity prediction model at master device to get the output class with probability score.
The problems aimed in this paper are different from the schemes presented in existing literature.
We integrated two different domains including MVS and activity recognition under the umbrella of a unified framework in an IoT environment.
We presented interconnected resource constrained IoT devices working together to achieve several targets i.e., object detection, summary generation, and activity recognition as shown in Figure 1 .
The overall framework consists of numerous slaves and a master resource constrained device connected through a common wireless sensor network (WSN).
The slave devices are equipped with a camera to capture multi-view video data, segment it into shots, generate summary, encode a sequence of keyframes, and transmit it to the master device.
The master device is equipped with an INTEL Movidius NCS to classify the ongoing activity in the acquired sequence.
INTEL Movidius is a modular and deep learning accelerator in a standard USB 3.0 stick.
It has a Vision Processing Unit (VPU) that is functional with ultra-low power and better performance.
It enables activity recognition with significantly lower power, storage, and computational cost.
Further, a widely used concept of temporal point processing (Xiao et al., 2019) is utilized for activity classification, ensuring an effective recognition model.
While addressing the problems in MVS and activity recognition over resource constrained devices, we made the following contributions.
• Employing an algorithm for MVS on resource constrained devices, reducing the time complexity compared to existing approaches with higher accuracy.
The generated summary is further utilized to recognize the underlying activity of all the views through an auto-encoder and learned spatio-temporal features followed by different variants of SVM classifiers to demonstrate the efficiency and effectiveness of our proposed framework.
• Adding uncertainties such as fog to an outdoor MVS benchmark dataset to demonstrate the working of proposed framework in any type of scenario and introduce a new trend in MVS literature for researchers.
• The presented framework has high-level adaptability with special care for the capacity and traffic of WSN.
It has many flavors with tradeoff among transmission time, quality of keyframes, and accuracy of activity recognition model with computationally different classifiers.
In the subsequent sections, Section 2 provides a literature review and Section 3 explains the presented framework in detail.
In Section 4, experimental results for MVS and activity recognition are given, and Section 5 concludes the overall paper with future directions.
In this paper, we integrated MVS and activity recognition under an umbrella of a unified framework.
A complete setup including slaves and a master resource constrained device working independently in an IoT is presented.
The hardware requirements include slave devices equipped with camera and wireless sensors, a master device with Intel Movidius NCS for running optimized deep learning models on the edge.
The slave devices capture multi-view video data, detect objects, extract features, compute mutual information, and finally generate summary.
The generated summary is received at master device with optimized trained model for activity recognition.
The MVS algorithm as well activity recognition models presented in this paper outperform state-of-the-art.
In future, we have intention to extend this work by deeply investigating multi-view action recognition algorithms with different parameters and configurations in resource constrained environments.
Further, we want to explore spiking neural networks used for various tasks [40, 41] in our framework for spatio-temporal features extraction advanced to activity recognition.

assistant: text: An efficient multi-view video summarization scheme advanced to activity recognition in IoT environments.