Solved: The 5 Challenges of Human Sensing

Researchers have identified five technology challenges in human sensing systems, all of which have been solved by Retail Sensing systems.

Thiago Teixeira, Gershon Dublon and Andreas Savvide of Yale University and Massachusetts Institute of Technology addressed the increasingly common requirement of computer systems to extract information about the people present in an environment. They pin-pointed five requirements that the most accurate systems need to meet.

1. Environmental Variations

Unexpected or sudden changes in environmental conditions are common sources of errors in some real-world scenarios. Radar signals, for instance, can be dampened by rain or fog. A large portion of the computer vision literature is aimed at dealing with variations in lighting, shadows and so on.

Our human sensing systems have been specifically designed to cope with situations with dark shadows and changes in lighting. Each sensing position can be configured for the particular conditions at that location. Tests show that video technology achieves over 98% accuracy in human sensing.

2. Similarity to Background Signal

“Clearly, separating a person from the background signal is a core requirement for human-sensing” the scientists write. Our technology freezes the background at the moment a person enters and easily distinguishes the moving person from her surroundings.

3. Appearance variability and unpredictability

People look different and wear a vast assortment of different types of clothes and hats, they push trollies and pushchairs, and carry back-packs and hand-bags. This at first looks like a problem for video systems, but is solved by the software converting an individual to a “blob”, as shown in the video below.

4. Similarity to Other People

Some tracking systems use identifying features of a person to track them – presenting a challenge if people are all wearing similar clothes or uniforms. Our technology follows the “blob” we have identified as person so it doesn’t matter if everyone looks alike. Each blob is seen as unique and distinguished from the others, even in crowded situations.

5. Active Deception

The researchers’ final point is when a human sensing system may be deliberately debilitated, perhaps by people walking slowly to fool motion sensors, covering a break beam with their hands or turning the lights off to fool the cameras. A CCTV system lets users remotely play back the video over the internet to see why counting has suddenly stopped and rectify the problem.

The researchers conclude that the best system is computer vision, saying “Computer vision is far ahead from other instrumented modalities not only with respect to spatial-resolution and precision metrics, but also in terms of having the most field-tested solutions“.

Further Reading

You can read the research at
A Survey of Human-Sensing: Methods for Detecting Presence, Count, Location, Track, and Identity T TEIXEIRA, G DUBLON, A SAVVIDES
ENALAB technical report

Windmill Software

Windmill Software specialise in data acquisition and control. They publish a monthly newsletter, called Monitor, giving useful information on sensors and systems -

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