## 6 Conclusions  
In this review, we provide a compressive survey of state-of-the-art human action representation and recognition approaches including both handcrafted and learning-based representations. The handcrafted action representation approaches have been there for a quite long time. These approaches have achieved remarkable results on different publically available bench mark datasets. However, most successful handcrafted representation methods are based on the local densely-sampled descriptors, which incur a high computational cost. In these approaches, the important features from the sequence of image frames are extracted to build the feature vector using human engineered feature detectors and descriptors. Then, the classification is performed by training a generic classifier. These approaches include space-time, appearance, local binary patterns, and fuzzy logic-based approaches.  
On the other hand, learning-based action representation approaches use trainable feature extractors followed by a trainable classifier, which lead to the concept of end-to-end learning or learning from pixel level to action categories identification. This eliminates the need for handcrafted feature detectors and descriptors used for action representation. These approaches include evolutionary (GP-based), dictionary learning, and deep learning-based approaches. Recently, the research community has paid a lot of attention to these approaches. This is mainly due to their high performance as compared to their handcrafted counterparts on some challenging datasets. However, fully data-driven deep models referred to as \"black-box\" have some limitations: Firstly, it is difficult to incorporate problem-specific prior knowledge into these models. Secondly, some of the best performing deep learning-based methods are still dependent on handcrafted features. The performance of the pure learning-based methods is still not up to the mark. This is mainly due to the unavailability of huge datasets for action recognition unlike in the object recognition where huge dataset such as ImageNet is available. Recently, a large scale dataset ActivityNet has been developed, which is expected to fill this gap. This dataset contains over 200 action categories with 849 hours of video data.  
In order to provide further insight into the field, we have presented the well-known public datasets for activity recognition. These datasets include: KTH, Weizmann, IXMAS, UCF Sports, Hollywood2, YouTube, HDMB-51, UCF-101, and ActivityNet. In addition to this, we have also presented the important applications of human activity recognition such as intelligent video surveillance, ambient assisted living, human-robot interaction, entertainment, and intelligent driving.  
**Acknowledgments:** This study is supported by the research grant of COMSATS Institute of Information Technology, Pakistan.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.