Retail in High Definition: Using Video Analytics in Salesforce Management

2015 ◽  
Author(s):  
Andres Musalem ◽  
Marcelo Olivares ◽  
Ariel Schilkrut
Keyword(s):  
High Definition ◽  
Video Analytics ◽  
Salesforce Management

scholarly journals Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge

2020 ◽  
Vol 10 (18) ◽  
pp. 6391
Author(s):  
Dien Van Nguyen ◽  
Jaehyuk Choi
Keyword(s):  
Data Extraction ◽  
Video Data ◽  
Transportation Safety ◽  
High Definition ◽  
Video Analytics ◽  
Surveillance Camera ◽  
Novel Approach ◽  
Cloud Server ◽  
Delay Sensitivity ◽  
Automated Tools

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.

Routine Digital Processing of Microscope Images

1990 ◽  
Vol 48 (1) ◽  
pp. 550-551
Author(s):  
E. Wisse ◽  
A. Geerts ◽  
R.B. De Zanger
Keyword(s):  
Operating System ◽  
Digital Processing ◽  
Hard Disks ◽  
The Sun ◽  
Tape Drive ◽  
High Definition ◽  
Fluorescent Microscope ◽  
Microscope Images ◽  
Helical Scan ◽  
Ethernet Connection

The slowscan and TV signal of the Philips SEM 505 and the signal of a TV camera attached to a Leitz fluorescent microscope, were digitized by the data acquisition processor of a Masscomp 5520S computer, which is based on a 16.7 MHz 68020 CPU with 10 Mb RAM memory, a graphics processor with two frame buffers for images with 8 bit / 256 grey values, a high definition (HD) monitor (910 × 1150), two hard disks (70 and 663 Mb) and a 60 Mb tape drive. The system is equipped with Imaging Technology video digitizing boards: analog I/O, an ALU, and two memory mapped frame buffers for TV images of the IP 512 series. The Masscomp computer has an ethernet connection to other computers, such as a Vax PDP 11/785, and a Sun 368i with a 327 Mb hard disk and a SCSI interface to an Exabyte 2.3 Gb helical scan tape drive. The operating system for these computers is based on different versions of Unix, such as RTU 4.1 (including NFS) on the acquisition computer, bsd 4.3 for the Vax, and Sun OS 4.0.1 for the Sun (with NFS).

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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