The concept of a real-time range camera without moving parts is described, based on the time-of-flight (TOF) principle. It operates with modulated visible and near-infrared radiation, which is detected and demodulated simultaneously by a 2-D array of lock-in pixels employing the charge-coupled device principle. Each pixel individually measures the amplitude, offset and phase of the received radiation. The theoretical resolution limit of this TOF range camera is derived, which depends on the square root of the detected background radiation and the inverse of the modulation amplitude. Actual measurements of 3-D sequences acquired at 10 range images per second show excellent agreement between our theory and the observed results. A range resolution of a few centimeters over a range of 10 m, with an illumination power of a few hundreds of milliwatts is obtained in laboratory scenes for noncooperative, diffusely reflecting objects.

A complete range camera system, working with the time-of- flight principle, is introduced. This ranging system uses modulated LEDs as active illumination source and a new lock-in CCD sensor as demodulator and detector. It requires no mechanically scanning parts because every pixel of the sensor contains a lock-in amplifier, enabling both intensity and range measurement for all pixels in parallel. Two such lock-in imagers are realized in 2.0 micrometer CMOS/CCD technology, (1) a line sensor with 108 pixels and an optical fill factor of 100% and (2) a 64 X 25 pixel image sensor with 20% fill factor. The basics of time-of-flight ranging are introduced with a detailed description of the elements necessary. Shot noise limitation to ranging resolution is deduced and confirmed by simulation. An optical power budget is offered giving the relation between the number of photons in a pixel depending on the light source, the observed object, and several camera parameters. With the described lab setup, non- cooperative targets can be measured over a distance of several meters with a resolution of some centimeters.

A new generation of smart pixels, so-called demodulation or lock-in pixels is introduced in this paper. These devices are capable of measuring phase, amplitude and offset of modulated light up to some tens of MHz, making them ideally suited to be used as receivers in 3D time-of-flight (TOF) distance measurement systems. Different architectures of such devices are presented and their specific advantages and disadvantages are discussed. Furthermore, a simple model is introduced giving the shot noise limited range resolution of a range camera working with these demodulation pixels. Finally, a complete TOF range camera based on an array of one of the new lock-in pixels will be described. This TOF- camera uses only standard components and does not need any mechanically scanning parts. With this camera non- cooperative targets can be measured with a few centimeters resolution over a distance of up to 20 meters.

Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.

Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes.