Gurumurthy Swaminathan

Multiple appearance models for face tracking in surveillance videos

Face tracking is a key component for automated video surveillance systems. It supports and enhances tasks such as face recognition and video indexing. Face tracking in surveillance scenarios is a challenging problem due to ambient illumination variations, face pose changes, occlusions, and background clutter. We present an algorithm for tracking faces in surveillance video based on a particle filter mechanism using multiple appearance models for robust representation of the face. We propose color based appearance model complemented by an edge based appearance model using the Difference of Gaussian (DOG) filters. We demonstrate that combined appearance models are more robust in handling the face and scene variations than a single appearance model. For example, color template appearance model is better in handling pose variations but they deteriorate against illumination variations. Similarly, an edge based model is robust in handling illumination variations but they fail in handling substantial pose changes. Hence, a combined model is more robust in handling pose and illumination changes than either one of them by itself. We show how the algorithm performs on a real surveillance scenario where the face undergoes various pose and illumination changes. The algorithm runs in real-time at 20 fps on a standard 3.0 GHz desktop PC.

Analog dial gauge reader for handheld devices

In a typical process industry, there are several critical dial gauges wherein the readings are to be monitored on a periodic basis. Most of these dial gauges are still analog in nature and currently these are being monitored manually. The manual capture of readings is a tedious task and is also prone to error. This paper outlines an algorithm that can automatically identify the dial gauge readings using image captured by a PDA device. This will help in reducing the error in readings as well to provide a record of the actual reading for future reference. Our algorithm uses polar representation of the dial gauge image to identify the needle position as well as the start position of the dial gauge. Once these are obtained the reading of the dial gauge can be estimated with prior calibration information. The algorithm was tested on multiple dial gauge images captured in a local chiller plant and is shown to obtain the readings reliably in about 95% of these images.

Aircraft push back direction indicator

One of the common ramp activities in an airport is the push-back operation. It involves an aircraft being pushed back off the airport terminal gates (in the apron area) and aligning it towards a taxiway leading to the designated runway prior to departure. The aircraft is assigned a designated taxiway depending on traffic conditions and once the aircraft is aligned with the corresponding taxiway, the taxiway is activated for taxing using ground lighting. There is a need to automatically identify the direction of the aircraft alignment after pushback so as to ensure correct alignment and also to activate the corresponding taxiway. In this paper, a novel real-time solution is proposed for automatically detecting the aircraft pushback direction time using feeds from existing video cameras installed at the gates. The algorithm achieves this objective by obtaining the most stable and significant optical flows from the scene sequence using motion-based segmentation and simultaneous calculation of their orientation direction. Algorithm is designed to perform well during day/nightconditions, in deteriorating climatic conditions with very poor visibility and at low resolution (160×120) and low fps (less than 5 fps).