Comparing Image Features and Machine Learning Algorithms for Real-Time Parking Space Classification

Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms

“…Finally, we want to focus on two additional aspects concerning the practical application in this study to improve our current system [14]:…”

“…In previous research, we build a system to classify vacant and occupied parking lots [14]. We run several test on our dataset containing approximately 10,000 samples for training and testing using color information in combination with other image features.…”

“…al. [14] ---space-driven methods Yamada and Mizuno [15] ---Funck et al [1] ? ---Lpez Sastre et al [17] ?…”

“…----Unfortunately, several papers do not report on processing speeds. Nevertheless, solutions that meet the requirement of high detection rates exist, e.g., [9], [14], [17], [18]. Each of those implements individual image features and machine learning algorithms that have been tested on different datasets with various camera perspectives.…”

“…We implemented several image features to test their ability to describe vacant and occupied parking lots in our previous work [14]. We now focus on color histograms and difference-of-Gaussian (DoG) histograms separately because those features yielded the most promising results in prior tests.…”