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Pedestrian and bicycle monitoring is quickly becoming an avid area of interest as information regarding pedestrian and bicycle flow is needed not only for developing competent access to particular urban corridors and trails, but also for system optimization scenarios, such as transit system operations and intersection controls. In this article, we present a simple, yet effective method for tracking pedestrian and bicycle objects in a relatively large surveillance area, using ordinary un-calibrated video images. Object extraction is accomplished via background subtraction, while tracking is accomplished through an inherent characteristic cost function. Composite objects are used as a means of dealing with occlusions. The algorithm is implemented using Microsoft Visual C# and was tested on numerous scenes of varying complexity, resulting in an average count rate of 92.7% at the specified checkpoints.

Signal cycle failure (or overflow) is an interrupted traffic condition in which a number of queued vehicles are unable to depart due to insufficient capacity during a signal cycle. Cycle failure detection is essential for identifying signal control problems at intersections. However, typical traffic sensors do not have the capability of capturing cycle failures. In this article, we introduce an algorithm for traffic signal cycle failure detection using video image processing. A cycle failure for a particular movement occurs when at least one vehicle must wait through more than one red light to complete the intended movement. The proposed cycle failure algorithm was implemented using Microsoft Visual C#. The system was tested with field data at different locations and time periods. The test results show that the algorithm works favorably: the system captured all the cycle failures and generated only three false alarms, which is approximately 0.9% of the total cycles tested.

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Neurons are the basic building and computational units of the nervous system, and have complex and diverse spatial geometric structures. By solving the neuronal classification problem, we can further understand the characteristics of neurons and the process of information transmission. This paper presents a neuronal morphology classification approach based on locally cumulative connected deep neural networks, where 43 geometric features were extracted from two different neuron datasets and applied to classify types of neurons. Then, the effects of different parameters of deep learning networks on the performance of neuron classification were analyzed including mini-batch size, number of intermediate layers, and number of building blocks. The accuracy of the approach was also compared with that of the other mainstream machine learning approaches. The experimental results showed that the proposed approach is effective for solving complex neuronal morphology classification problems.