Activity recognition

Summary

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology. Due to its multifaceted nature, different fields may refer to activity recognition as plan recognition, goal recognition, intent recognition, behavior recognition, location estimation and location-based services. Types Sensor-based, single-user activity recognition Sensor-based activity recognition integrates the emerging area of sensor networks with novel data mining and machine learning techniques to model a wide range of human activities. Mobile devices (e.g. smart phones) provide sufficient sensor

Official source

https://en.wikipedia.org/wiki/Activity_recognition

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A 170μW Image Signal Processor Enabling Hierarchical Image Recognition for Intelligence at the Edge

Bowen Liu

We propose an ultra-low power (ULP) Image Signal Processor (ISP) that performs on-the-fly in-processing frame (de)compression and hierarchical event recognition to exploit the temporal and spatial sparsity in an image sequence to achieve a 16× imaging system energy gain. The ISP is fabricated in 40 nm CMOS and consumes only 170 μW at 5 fps for neural network-based intruder detection and 192× compressed image recording.

IEEE2020

We propose an ultra-low-power (ULP) image signal processor (ISP) that performs on-the-fly in-processing frame compression/decompression and hierarchical event recognition to exploit the temporal and spatial sparsity in an image sequence. This approach reduces energy consumption spent processing and transmitting unimportant image data to achieve a 16 × imaging system energy gain in an intruder detection scenario. The ISP was fabricated in 40-nm CMOS and consumes only 170 μW at 5 frames/s for neural network-based intruder detection and 192 × compressed image recording.

IEEE2020

Workflow Monitoring based on Multi-view Reconstruction

Daniel Weinland

Activity recognition has primarily addressed the identification of either actions or well-defined interactions among objects in a scene. In this work, we extend the scope to the study of workflow monitoring. In a workflow, ordered groups of activities (phases) with different durations take place in a constrained environment and create temporal patterns across the workflow instances. We address the problem of recognizing phases, based on exemplary recordings. We propose to use Workflow-HMMs, a form of HMMs augmented with phase probability variables that model the complete workflow process. This model takes into account the full temporal context which improves on-line recognition of the phases, especially in case of partial labeling. Targeted applications are workflow monitoring in hospitals and factories, where common action recognition approaches are difficult to apply. To avoid interfering with the normal workflow, we capture the activity of a room with a multiple-camera system. Additionally, we propose to rely on real-time low-level features (3D motion flow) to maintain a generic approach. We demonstrate our methods on sequences from medical procedures performed in a mock-up operating room. The sequences follow a complex workflow, containing various alternatives.

2009