Embedded Camera System Research Articles

A Post-Scan Point Cloud Colorization Method for Cultural Heritage Documentation

The 3D laser scanning technique is important for cultural heritage documentation. The laser itself normally does not carry any color information, so it usually requires an embedded camera system to colorize the point cloud. However, when the embedded camera system fails to perform properly under some external interferences, a post-scan colorization method is always desired to improve the point cloud visuality. This paper presents a simple but efficient point cloud colorization method based on a point-to-pixel orthogonal projection under an assumption that the orthogonal and perspective projections can produce similar effects for a planar feature as long as the target-to-camera distance is relatively short (within several meters). This assumption was verified by a simulation experiment, and the results show that only approximately 5% of colorization error was found at a target-to-camera distance of 3 m. The method was further verified with two real datasets collected for the cultural heritage documentation. The results showed that the visuality of the point clouds for two giant historical buildings had been greatly improved after applying the proposed method.

Detecting Convoys Using License Plate Recognition Data

License plate recognition (LPR) sensors are embedded camera systems that monitor road traffic. When a vehicle passes by a sensor, the vehicle's license plate, the location, and the time of observation are recorded. Given a stream of such observations from a collection of sensors spread around the road network, our goal is to detect convoys: groups of two or more vehicles traveling with highly correlated trajectories. Some of the main challenges with modeling and processing data from LPR sensors include that the data-gathering process is event-driven, thus data are not regularly sampled in time or space. Also, an appropriate definition of convoy should be relative to background traffic patterns, which are temporally and spatially varying. This paper proposes novel models for LPR observations of traffic which are well suited for online convoy detection. Baseline traffic is modeled as following a mixture of semi-Markov processes, and specific models for temporal and spatial correlation of observations of vehicles traveling in a convoy are introduced. These models are used within a sequential hypothesis testing framework to obtain a system for real-time convoy detection. The model of baseline traffic may be of independent interest for forecasting road traffic patterns. Experiments with an extensive simulated dataset illustrate the performance of the scheme and offer insights into the tradeoffs between detection rate, false alarm rate, and the expected number of observations required to detect a convoy.

Ubiquitous luminance sensing using the Raspberry Pi and Camera Module system

In this paper, the authors have calibrated a Raspberry Pi and Camera Module (RPiCM) for use as an absolute luminance sensor. The spectral response of the RPiCM chip as well as linear mapping to the standard CIE-XYZ colour space have been measured, calculated and presented. The luminance values are anchored to absolute luminance measurements. Further, by using high dynamic range imaging techniques making use of different shutter speeds in a sequence of images, the measurement of luminance values from approximately 10 to 50,000 cd/m2 is possible. Lens correction for vignetting is also addressed, while pixel point spreading is ignored. This measurement goes beyond a single point measurement, economically and accurately allowing each of the arrays within the RPiCM chip to act as an individual luminance meter over the entire field of view of the camera system. Applications and limitations of the embedded camera system are discussed. An Energy Plus model is constructed as a motivational application of a simple one room, one window space and simulated for a year using weather files from around the world. These simulations highlight the need for spatial luminance-based sensing within the built environment to counteract the experience of discomfort glare by building occupants.

Detecting Laser Spot In Shooting Simulator Using An Embedded Camera

Abstract This paper presents the application of an embedded camera system for detecting laser spot in the shooting simulator. The proposed shooting simulator uses a specific target box, where the circular pattern target is mounted. The embedded camera is installed inside the box to capture the circular pattern target and laser spot image. To localize the circular pattern automatically, two colored solid circles are painted on the target. This technique allows the simple and fast color tracking to track the colored objects for localizing the circular pattern. The CMUCam4 is employed as the embedded camera. It is able to localize the target and detect the laser spot in real-time at 30 fps. From the experimental results, the errors in calculating shooting score and detecting laser spot are 3.82% and 0.68% respectively. Further the proposed system provides the more accurate scoring system in real number compared to the conventional integer number.

Mobile image sensors for object detection using color segmentation

The intelligent mobile robot with sensors and image processing embedded system combines the suction and aerodynamic attraction to achieve good balance between strong adhesion force and high mobility. Experimental results showed that the robot can move upward on the wall at the speed of 2.9 m/min and carry 5 kg payload in addition to 2.5 kg self-weight, which record the highest payload capacity among climbing robots of similar size. It also implements object detection ability using effective color transform and segmentation technique for the exact target detection on the wall using a embedded camera system, communication module and several active sensors.

Object tracking for mobile robot based on intelligent method

This paper proposed the real-time tracking algorithm in the active camera system, which is based on the intelligent method. To separate the object from background, the similarity of the color is analyzed through the fuzzy inference engine. And after segmentation, the local difference method between the continuous images is used to track the moving object. The experiment is performed using the developed embedded camera system with an ARM processor.