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With advances in machine learning, the evaluation and analysis of human behavior continue to attract large number of researchers around the globe. In this paper, we furnish an extensive overview of ways to identifying, analyzing and assessing human behavior, taking into account various behavioral characteristics. Most promising attributes and recognition techniques for vision and sensor-based approaches have been detailed. Most prominently used datasets for both vision- and sensor-based approaches have also been studied, keeping in mind the nature, source and applications of the same in the field of human behavior and activity detection. The study indicates that sensor-based approaches tend to have an upper hand because of the privacy breach caused by vision-based approaches, which accounts for the evolving usage of sensor-based monitoring for real-time behavior detection. Various other deep learning methods and their applications in the field of behavioral recognition have also been stated.