# Human Attribute Recognition--
## 6 Discussion
### Discussion Over HAR Datasets
However, in these images, the persons are aware of being photographed and perhaps even prepared for this (posing for the image, dressed up nicely for a photo session, etc.). Therefore, even if some datasets contain _in-the-wild_ images gathered for a different system, they might still contain important differences from _real-world_ images in which the subject is unaware of being photographed, the image is captured automatically, without any human intervention, are the subjects are dressed normally and performing natural dynamic movements. 6. Pose and occlusion labeling. Another nice to have feature for a HAR dataset is the annotation of pose and occlusions. Some databases already provide this information [66, 78, 108, 153]. Amongst other things, these extra labels prove useful in the evaluation of HAR systems, as they allow researchers to diagnose the errors of HAR and examine the influence of various factors. 7. Data partitioning strategies. When dealing with HAR, the datasets partitioning scheme (into the train, validation, and test splits) should be carefully engineered. A common pitfall is to split the frames into the train and validation splits randomly, regardless of the person's identity. This can lead to an unfair assignment of a subject into one of these splits, and inducing bias in the evaluation process. This is even more important, as the current state-of-the-art methods generally rely on deep neural network architectures, which have a black-box behavior in nature, and it is not so straightforward to determine which image features lead to the final classification result. Solutions to this problem include extracting each individual (along with its track-lets) from the video sequence or providing the annotations at the identity level. Then, each person could be randomly assigned to one of the dataset splits. 8. Synthetic data. Recently, significant advances have been made in the field of computer graphics and synthetic data generation. For example, in the field of drone surveillance, generated data [173] has proven its efficiency in training accurate machine vision systems. In this section, we have presented some computer-generated datasets which contain human attribute annotations. We consider that synthetically generated data is worth taking into consideration, as theoretically, it can be considered an inexhaustible source of data, which could be able to generate subjects with various attributes, under different poses, in diverse scenarios. However, state-of-the-art generative models rely on deep learning, which is known to be \"hungry\" for data, so data is needed to build a realistic generative model. Therefore, this solution might prove to be just aicious circle. 9. Privacy issues. In the past, as traditional video surveillance systems were simple and involved only human monitoring, privacy was not a major concern; however, these days, the pervasiveness of systems equipped with cutting-edge technologies in public places (e.g., shopping malls, private and public buildings, bus and train stations) have aroused new privacy and security concerns. For instance, Privacy Commissioner of Canada (OPC) is an organization that helps people report their privacy concerns and enforces the enterprises to manage people's personal data in their business activities based on restricting standards ([[https://www.priv.gc.ca/en/report-a-concerm/](https://www.priv.gc.ca/en/report-a-concerm/)]([https://www.priv.gc.ca/en/report-a-concerm/](https://www.priv.gc.ca/en/report-a-concerm/))). When gathering a dataset with real-world images, we deal with privacy and human rights violations. Ideally, HAR datasets should contained images captured by real-world surveillance cameras, with the subjects are unaware of being filmed, such that their behavior is as natural as possible. From an ethical perspective, humans should consent before their images are annotated and publicly distributed. However, this is not feasible for all scenarios.