Soroush Shahi

soroush@northwestern.edu

Suite 1400

680 N. Lakeshore Dr.

Chicago, IL 60611

I am a graduate student at Computer Science Department of Northwestern University. My research is focused on human activity recognition using wearable devices such as wearable cameras. I am interested in detecting activities that has implications for human health such as eating and smoking. Recently, I worked on addressing privacy aspects of wearable cameras using image obfuscation techniques. Prior to joining Northwestern University, I got a Bachelor of Science in Computer Engineering from the University of Tehran.

CV/Resume | Google Scholar | GitHub

selected publications

Impacts of Image Obfuscation on Fine-grained Activity Recognition in Egocentric Video

Shahi, Soroush, Alharbi, Rawan, Gao, Yang, Sen, Sougata, Katsaggelos, Aggelos K, Hester, Josiah, and Alshurafa, Nabil

In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) Mar 2022

Abs

Automated detection and validation of fine-grained human activities from egocentric vision has gained increased attention in recent years due to the rich information afforded by RGB images. However, it is not easy to discern how much rich information is necessary to detect the activity of interest reliably. Localization of hands and objects in the image has proven helpful to distinguishing between hand-related fine-grained activities. This paper describes the design of a hand-object-based mask obfuscation method (HOBM) and assesses its effect on automated recognition of fine-grained human activities. HOBM masks all pixels other than the hand and object in-hand, improving the protection of personal user information (PUI). We test a deep learning model trained with and without obfuscation using a public egocentric activity dataset with 86 class labels and achieve almost similar classification accuracies (2% decrease with obfuscation). Our findings show that it is possible to protect PUI at smaller image utility costs (loss of accuracy).
Detecting Screen Presence with Activity-Oriented RGB Camera in Egocentric Videos

Adate, Amit, Shahi, Soroush, Alharbi, Rawan, Sen, Sougata, Gao, Yang, Katsaggelos, Aggelos K, and Alshurafa, Nabil

In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) Mar 2022

Abs

Screen time is associated with several health risk behaviors including mindless eating, sedentary behavior, and decreased academic performance. Screen time behavior is traditionally assessed with self-report measures, which are known to be burdensome, inaccurate, and imprecise. Recent methods to automatically detect screen time are geared more towards detecting television screens from wearable cameras that record high-resolution video. Activity-oriented wearable cameras (i.e., cameras oriented towards the wearer with a fisheye lens) have recently been designed and shown to reduce privacy concerns, yet pose a greater challenge in capturing screens due to their orientation and fewer pixels on target. Methods that detect screens from low-power, low-resolution wearable camera video are needed given the increased adoption of such devices in longitudinal studies. We propose a method that leverages deep learning algorithms and lower-resolution images from an activity-oriented camera to detect screen presence from multiple types of screens with high variability of pixel on target (e.g., near and far TV, smartphones, laptops, and tablets). We test our system in a real-world study comprising 10 individuals, 80 hours of data, and 1.2 million low-resolution RGB frames. Our results outperform existing state-of-the-art video screen detection methods yielding an F1-score of 81%. This paper demonstrates the potential for detecting screen-watching behavior in longitudinal studies using activity-oriented cameras, paving the way for a nuanced understanding of screen time’s relationship with health risk behaviors.