For a complete changelog of our releases, please refer to this article
Vidireports 7.9.0.0
Support for series 6 BrightSign players
We now support the following models:
- HD6 (HD1026)
- XD6 (XD1036)
- XS156
For more detail about BrightSign, please refer to this article
Vidireports 7.8.1.0
Retail Analytics
Retail Analytics is a feature which allows to collect audience metrics on selected areas of the video stream. This can be used to extract precise information about the traffic in a zone of the scene.
For more detail about BrightSign, please refer to this article
Vidireports 7.7.8.0
Support for series 5 BrightSign players
We now support the following models:
- LS5 (LS425, LS445)
- XD5 (XD1035)
- XT5 (XT1145, XT2145)
For more detail about BrightSign, please refer to this article
Compass measurement added
Quividi provides customers with a way to analyze the direction of foot traffic in front of a camera. Dubbed "Compass", this system can enrich their Digital Out of Home (DOOH) or Digital Signage network, with 2 important metrics: the traffic direction in relation to the camera, and the time spent in a given region.
For more detail about Compass, please refer to this article
Vidireports 7.7.0.0
Footfall on entry-level ARM architectures
ARM processors are gaining significant market traction in digital signage players and have become quite the norm in SoC displays, allowing for competitively-priced solutions. The relatively limited performance of the CPU (Computing Processing Unit) is offset by an on-board GPU (Graphical Processing Unit) that is used to smoothly render videos even at high resolution.
Quividi’s VidiReports is designed to run solely on the CPU in order not to interfere with the video rendering process; this works greatly on sufficiently powerful architectures, (typically, any Intel-based PC) but, on ARM devices, the limited available resources have so far induced a performance penalty in our software and have prevented us from leveraging state-of-the art inference engines for computer vision. As a result, ARM-based deployments have traditionally been more limited in features and they have been usually reserved for less demanding audience measurement projects.
But all of this is now in the past: with VidiReports 7.7, Quividi has started integrating a new cutting-edge inference engine for real-time object detection that is designed to run in real-time on Cortex A ARM processors. This has been made possible thanks to a recently announced partnership with Plumerai. This solidifies Quividi’s strategy of leveraging the best-in-class computer vision technologies into our robust, universal platform, which we officially inaugurated in 2019 with the integration of Intel’s OpenVINO engine.
The Plumerai inference engine runs in parallel to Quividi’s own face detection and classification engines (VidiReports Pro), and is offered at no additional cost to our customers using ARM processors, on Linux (not available at this stage under Android).
What this means for our customers is that they can now count impressions in real time, at long distances, with precise dwell time, even on low-power and midrange players; examples include the BrightSign players XT, XD 4 and above, LG WebOS screens, as well as the the myriad players whose processor is at least a Cortex A 53; this includes anything built around a Raspberry Pi4, for instance.
Whether in vending machines, digital merchandising displays, or retail media screens, virtually everyone is now eligible for programmatic trading, using the same precise and highly granular impression multipliers that Quividi’s software has been delivering to top platforms worldwide.
Unified Tracker
With real-time body detection now being available on Intel and Arm processors alike, we can be more precise in the tracking of people and have merged the tracker used to detect bodies and the tracker used to detect faces.
Indeed, in computer vision, the correct counting of audiences proceeds from a mix of correctly detecting a face or a body and tracking it over time as long as it appears in the field of view of the camera. By assuming that as long as we detect a body (with 95%+ accuracy), a head is above it, we can better estimate the unique viewers, as well as correctly estimate the dwell time and attention time of each person.
VidiReports was precisely counting audiences in locations with people on the go, but until this version 7.7, long dwell time places (like waiting rooms or taxis) were challenging scene types. This is now correctly covered by our solution.