Pedestrian-motorcycle binary classification using data augmentation and convolutional neural networks

One common problem in vehicle and pedestrian detection algorithms is the mis-classification of motorcycle riders as pedestrians. This paper focused on a binary classification technique using convolutional neural networks for pedestrian and motorcycle riders in different road context locations. The study also includes a data augmentation technique to address the un-balanced number of training images for a machine learning algorithm. This problem in un-balanced data sets usually cause a prediction bias, in which the prediction for a learned data set usually favors the class with more image representations. Using four data sets with differing road context (DS0, DS3-1, DS4-3, and DS4-3), the binary classification between pedestrian and motorcycle riders achieved good results. In DS0, training accuracy is 96.96% while validation accuracy is 81.52%. In DS3-1, training accuracy is 93.17% while validation accuracy is 86.58%. In DS4-1, training accuracy is 94.42% while validation accuracy is 97.00%. In DS4-3, training accuracy is 95.94% while validation accuracy is 88.59%. © 2019, Springer Nature Switzerland AG

An Agent-Based Solution for the Problem of Designing Complex Ambient Intelligence Systems

International audienc

Visual percepts quality recognition using convolutional neural networks

In visual recognition systems, it is necessary to identify between good or bad quality images. Visual perceptions are discrete representation of observable objects. In typical systems, visual parameters are adjusted for optimal detection of good quality images. However, over a wide range of visual context scenarios, these parameters are usually not optimized. This study focused on the learning and detection of good and bad percepts from a given visual context using a convolutional neural network. The system utilized a perception-action model with memory and learning mechanism which is trained and validated in four different road traffic locations (DS0, DS3-1, DS4-1, DS4-3). The training accuracy for DS0, DS3-1, DS4-1, and DS4-3 are 93.53%, 91.16%, 93.39%, and 95.76%, respectively. The validation accuracy for DS0, DS3-1, DS4-1, and DS4-3 are 88.73%, 77.40%, 95.21%, and 83.56%, respectively. Based from these results, the system can adequately learn to differentiate between good or bad quality percepts. © 2020, Springer Nature Switzerland AG. © 2020, Springer Nature Switzerland AG