Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

<p>In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.</p><p>In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. </p><p>In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.</p>

Reptile Recognition based on Convolutional Neural Network

Indonesian people are less interested in reptile animals. These are because most Indonesian people have the mindset that reptiles are difficult to tame and are focused on things about the ferocity of these animals in their natural habitat. Therefore it is necessary to have the means to identify reptile objects as one of the educational tools for introducing reptiles to the public. This research aims to produce a specialized Convolutional Neural Network model for recognizing reptile species. We also expand the model for recognizing another reptile species such as Snake, Crocodile, Turtle, and Gecko. Thousands of reptile images are being trained inside our model in order to obtain a kernel that can be used to automate reptile species recognition based on ordinary camera images. Our model currently reaches 64.3% accuracy for detecting 14 different species. Finally, as a suggestion for the next research, further enrichment especially from the background extraction process is needed to increase the accuracy of reptile detection.