With the advent of the Internet of Everything, more and more devices are connected to the Internet every year. In major cities, in order to maintain normal social order, the demand for deployed cameras is also increasing. In terms of public safety, person Re-Identification (ReID) can play a big role. However, the current methods of ReID are to transfer the collected pedestrian images to the cloud for processing, which will bring huge communication costs. In order to solve this problem, we combine the recently emerging edge computing and use the edge to combine the end devices and the cloud to implement our proposed binarized segmented ResNet. Our method is mainly to divide a complete ResNet into three parts, corresponding to the end devices, the edge, and the cloud. After joint training, the corresponding segmented sub-network is deployed to the corresponding side, and inference is performed to realize ReID. In our experiments, we compared some traditional ReID methods in terms of accuracy and communication overhead. It can be found that our method can greatly reduce the communication cost on the basis of basically not reducing the recognition accuracy of ReID. In general, the communication cost can be reduced by four to eight times.

https://www.mdpi.com/1424-8220/20/23/6902/pdf

A Binarized Segmented ResNet Based on Edge Computing for Re-Identification.

The task of video object segmentation is a fundamental but challenging problem in the field of computer vision. To deal with large variations in target objects and background clutter, we propose an online adaptive video object segmentation (VOS) framework, named Meta-VOS, that learns to adapt the target-specific segmentation. Meta-VOS builds an online adaptive learning process by exploiting cumulative expertise after searching for confidence patterns across different videos/frames, and then dynamically improves the model learning from two aspects: Meta-seg learner (i.e., module updating) and Meta-seg criterion (i.e., rule of expertise). As our goal is to rapidly determine which patterns best represent the essential characteristics of specific targets in a video, Meta-seg learner is introduced to adaptively learn to update the parameters and hyperparameters of segmentation network in very few gradient descent steps. Furthermore, a Meta-seg criterion of learned expertise, which is constructed to evaluate the Meta-seg learner for the online adaptation of the segmentation network, can confidently online update positive/negative patterns under the guidance of motion cues, object appearances and learned knowledge. Comprehensive evaluations on several benchmark datasets demonstrate the superiority of our proposed Meta-VOS when compared with other state-of-the-art methods applied to the VOS problem.

Meta-VOS: Learning to Adapt Online Target-Specific Segmentation

The use of computerized, digital video as a means for interactive data capture has been suggested as an alternative to direct observation of behavior. The appeal of observational measures is that they are presumed to be less vulnerable to potential biases from informants, such as parents or teachers, and permit more individualized assessment that may be lost with the use of rating scales. As a potential tool for long-term, automated observation and analysis. In this technology review we propose one promising model for the integration of computerized primitives recognition and annotated video patterns as an approach to large-scale autism diagnosis and research.

Integrating Computerized Primitives and Annotated Video Patterns: A Proposed Model for Autism Diagnosis and Research

This study proposes a novel inference adaptive thresholding based non-maximum suppression (NMS) (IAT-NMS) algorithm for deriving temporal cues between video sequences. The inference of temporal connectivity is first derived according to an overlapping measure of the bounding boxes between adjacent frames. Frames with high-confidence detection object are taken as key frames to leverage the scores of neighbor detections and preserve potential detections of blurred objects with low scores. Then, bounding boxes within each frame are ranked via their confidence scores and the overlapping ratio between the bounding box with the highest score against the remaining surrounding boxes is computed. This measure of overlapping is brought into a Gaussian function to estimate weights for adaptive suppression and to softly suppress the detection scores of possible severely overlapped objects. The proposed method is compared with state-of-the-art video object detection techniques. With the application of IAT-NMS, overlapping objects originally undistinguishable in the compared methods become detectable. Experimental results demonstrate that this simple and unsupervised method outperforms state-of-the-art NMS algorithms, with an increase of 6% in mean average precision (mAP) on the ImageNet VID dataset. Our study on performance limitations and sensitivity to parametric variations also finds that IAT-NMS demonstrates better detection capability than does the three compared algorithms, which fail to detect all targets or distinguish in the presence of multiple overlapping targets.

Inference Adaptive Thresholding based Non-Maximum Suppression for Object Detection in Video Image Sequence

Deep neural network (DNN)-based video processing methods are applied in mobile video analytics because of high accuracy. Edge computing is an efficient paradigm that improves the performance of mobile video analytics. However, due to the limited computing and storage resources at edge devices, deploying DNN-based video analytics at edge devices may have difficulty to meet user’s requirements in terms of accuracy, delay, power consumption, and device costs. Choosing optimal system configuration, including resources on edge devices and parameters in video stream and DNN models, can better satisfy user’s performance requirements; however, there lacks practical approaches to find such optimal configurations. In this article, we take an initial step to investigate the optimal system configuration problem, and propose a data-driven approach, EdgeEye, which first models the above problem as a combinatorial optimization problem, and then designs an algorithm to find the solutions for the optimal configuration. These models and algorithms are applied in a real-world face detection and recognition application based on two edge computing models, including edge only and edge server. Comprehensive evaluation results demonstrate that EdgeEye can find both feasible and optimal system configurations including optimal edge computing model to satisfy varying user requirements under different network conditions.

EdgeEye: A Data-Driven Approach for Optimal Deployment of Edge Video Analytics

Video surveillance system is set up and 21, being calibrated 22, tasking and 23 is operated (24). The system extracts an event from the video primitives using an extracting video primitives and 42, the event determination (44). The system may based on the extracted event performing a response (45).

Video surveillance system employing video primitives

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Video surveillance system employing video primitives