History and Background:
Human Activities and Intelligent Machine: Human activity is the set of actions a human performs in his daily routine. This set of actions can be as simple as standing or sitting or as complicated as cooking or writing. Human activities place an important impact on their environment and the environment makes changes according to these activities. Hence, these activities shall be monitored and understood in order to predict or analog the behavior the environmental behavior.
Increase of population:
With the increase of population and relevant chores, machines are being used to supplant the jobs of humans. Such as before the dissemination of threshers, farmers use to cut the crops on their own which use to be very time-consuming and acquire a lot of human effort. For the past few decades, researchers are focussing on making intelligent machines that analog human behavior in particular jobs and circumstances. For example, monitoring the public areas for safety purposes is employed installation of CCTV cameras that observe human controllers. This observation is now days shifted towards the responsibility of intelligent algorithms. These algorithms replace monotonous human efforts. Therefore, this century can be regarded as the era of artificial intelligence.
Artificial intelligent systems:
Artificial intelligent systems employ data perception, analysis, learning, and modeling in order to correctly engineer the features of activity for classification or detection tasks. This detection and classification of human activities are very crucial for many automated tasks such as the automatic generation of natural language of videos, automatic surveillance, and manufacturing of autonomous robots and vehicles. For example, in the case of autonomous vehicles, it is imperative to discern the activities of pedestrians so that decision of speed or movement can be made. If we take the example of an automatic surveillance system, suspicious or abnormal activities are very sensitive. If these activities are predicted correctly, precautionary measures can be taken on time.
Make machines intelligent:
In order to make machines intelligent enough to deduce correct information, machines are exposed to experience. Intelligent machines learn from their experiences. This experience is gained through data. In other words, a child learns to recognize a train if he is continuously shown the train or images of the train, which can be regarded as experience. Hence, to make intelligent machine learning systems capable of classifying or detecting human activities, machines must encounter the activity data and learn how a particular activity looks like. In the case of visionary data, videos that consist of human activities are required to train a machine learning algorithm. This dataset must be collected with favorable and relatable conditions in order to make intelligent systems performance error-free.
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