Complex tasks have three criteria:

It requires more than two object-based models for classification and detection. For example, A restaurant owner wants to check if his employees are wearing a hairnet in the kitchen, segregated by colours for different food types. To detect this, first, a head-detection has to be done, then a hairnet, followed by the colour of the hairnet. Here, three object-based detections happen, i.e., a head, a hairnet, and the colour of the hairnet.

It has a mix of objects and activity-based models. For example, a retail store manager wants to check if mopping happens at her store at different times. Such a task becomes a mix of objects and activity-based detection. In this task, after the AI detects a human, it will check if the human is holding a mop, after which it will check for the activity (motion) of mopping. In this example, there are two object-based (human and mop) and one activity-based (mopping activity) detection.

It has only activity-based detections. This scenario could involve only one activity like raised hands in a bank or multiple activity-based detections.

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