A Review on Abnormal Activity Detection Methods

Ahir, Alisha; Pateriya, Pushpendra Kumar; Kaur, Devinder; Kumar, Mritunjay

doi:10.1109/ctems.2018.8769128

Cited by 7 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With its resulted outcome actions are identified using 3DCNN and LSTM. Visual-temporal features help in classifying the final outcome either as normal or abnormal actions [12].…”

Section: System Designmentioning

confidence: 99%

Abnormality Detection in Human Action Using Thermal Videos

Soundararajan

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

Anomaly detection is a challenging task in the surveillance system due to the factors like extracting appropriate features, inappropriate differentiation among the normal vs abnormal behaviours, the sparse occurrence of abnormal activities and environmental variations. In the dark environment, detection of human actions is still difficult as more features for recognizing the key point are not visible. Hence the proposed work is focused on overcoming the environmental variations task that too in a less bright environment by using thermal videos. Variations in the actions can be easily identified as it works on the property of infrared radiations. For recognizing actions, the skeleton-based approach is used as it helps with the joint-wise segregation of human parts, resulting in more accuracy. The motion pattern of humans in the thermal video is tracked to classify the level of abnormality.

show abstract

“…With its resulted outcome actions are identified using 3DCNN and LSTM. Visual-temporal features help in classifying the final outcome either as normal or abnormal actions [12].…”

Section: System Designmentioning

confidence: 99%