This paper presents a survey on dynamic object detection in video sequences. Many methods have been proposed during several years that intent to solve the problems of dynamic background, jittering, illumination changes, camouflage, among other. The survey is intended to provide a recent analysis of these methods to highlight the main characteristics of each approach. The survey is based on published paper from 2013 up to date. 77 methods were found, and they are described and analyzed in this survey. In addition, the survey proposes a complete classification of the methods based on the type of technique used to achieve the detection of dynamic objects. It also evaluates the limitations of each approach and the tendencies regarding the most used techniques, color space, datasets, hardware, programming language and processing time. One of the most popular approaches correspond to advanced statistical models, and artificial neural networks with 28% and 22% respectively. The analysis reports 11% of use of GPUs, most of them found in the neural network approaches. Regarding datasets, there are authors that report the use of three datasets the most popular is Change Detection. The most used software to implement the methods is MATLAB and the RGB color space, the most employed.