Department Publications

Human activity recognition finds many applications in areas such as surveillance, and sports. Such a system classifies a spatio-temporal feature descriptor of a human figure in a video, based on training examples. However many classifiers face the constraints of the long training time, and the large size of the feature vector. Our method, due to the use of an Support Vector Machine (SVM) classifier, on an existing spatio-temporal feature descriptor resolves these problems in human activity recognition. Comparison of our system with existing classifiers using two standard datasets shows that our system is much superior in terms of the computational time, and either it surpasses or is on par with the existing recognition rates. It performs on par or marginally inferior to existing systems, when the number of training examples are a few due to the imbalance, although consistently better in terms of computation time.

Feature descriptors have enabled feature matching under varying imaging conditions, while mostly being backed by experimental evidence. In addition to imposing some restrictions in imaging conditions needed to ensure matching, extending the existing descriptors is not straightforward due to the lack of sound mathematical bases. In this work, by using a surface bending versus shape histogram based on the principal curvatures, we are able to produce a descriptor which is not sensitive to the errors in dominant orientation assignment. Experimental evaluations show that our descriptor outperforms existing descriptors in the areas of viewpoint, rotation, scale, zoom, lighting and compression changes, with the exception of resilience to blur. Further, we apply this descriptor for accuracy demanding applications such as homography estimation and pose estimation. The experimental results show significant improvements in estimated homography and pose in terms of residual error and Sampson distance respectively.

Developing autonomous mobile robots that can coexist with human in populated environments is still considered a big challenge. To address this problem the authors propose a novel scheme to assist mobile robots by providing localization information externally. This scheme combines the autonomous navigation and target tracking research fields to arrive at a structured assistance system for autonomous mobile robots. In the proposed scheme, the environment is sensed using a laser range finder and a camera based sensor unit. Using the Rao-Blackwellized particle filter technique, the robots that need assistance are continuously tracked. In contrast with conventional laser range finder based tracking systems, the placement of the sensor is changed to a level above average human height and the mobile robots are modified by attaching a cylindrical pole. The experiments showed the validity of the proposed …

Motion Blur due to the relative motion between the camera and object can seriously degrade image quality. We present an FPGA based motion blur detection and correction algorithm which is implemented on top of a configurable soft-processor based architecture. The system consists of two main modules. The blur detection module identifies the blur length and angle, and the restoration module uses regularized inverse filtering to remove the motion blur. The Processing algorithms are implemented as separate cores on the FPGA fabric where the soft processor core is only used for managing system configuration. The system can achieve a frame rate of 15fps for a 720p HD video stream.

In this work we focus on the problem of detecting vehicles that come to a stop, in a video of approximately 15 frames per second. The videos are retrieved by surveillance cameras fixed as shown in Fig. 1 which are located few meters from the ground.

Increasing use of CCTV for city and building surveillance has given rise to an environment where an object (a person) might traverse through the field of vi>vv of many cameras. In this paper we explore the problem of tracking multiple objects in a multi camera environment, which is a highly addressed area in computer vision. Our research involves real time tracking of objects while they are moving in a multi camera environment with non-overlapping field of v/ewjr and detecting them when they re-uppear in the same or another camera in the same system. Previous methods of using offline trained classifiers with huge databases are time consuming and have the drawback of incapability of detecting arbitrarily selected objects. We address this issue by online training with the initial sample given and is based on the TLD (Tracking, Learning, Detection) framework. We extend the idea to formulate our methodology to …

In computer vision crass-correlation is a standard approach for local feature matching in feature tracking applications. For this Normalized cross-correlation (NCC) is implemented on spatial domain, because it does not have a simple and efficient frequency domain expression. Therefore in applications which demand real-time processing, a dedicated hardware implementation of NCC is essential to meet the computational cost in spatial domain. In this paper we present a Field Programmable Gate Array (FPGA) implementation of NCC based on the multi-port memory controller together with Fast Normalize Cross-correlation. Practical experimentation shows that our system can achieve frame rates closer to 100 for a search window size of 100x100 and template size of 15x15, with only using two dual-port memories.

Augmented Reality (AR) is one of the emerging technologies being used for enhancing user experience in many applications. Specific devices for various tasks are been replaced by Smart Phones making them a unique platform to implement all functionalities in one device. In this paper we present a real-time AR framework for mobile devices that takes into consideration key technical challenges such as limited processing power and battery life. The framework is having four separate modules, Marker Detection, Identification, Camera pose calculation and Embedding visual information. A 2D pattern called a marker is used to uniquely identify the objects. The marker in the camera view is detected and tracked so that information contained in the marker is exploited. Tracked four corners of the marker are used to calculate the 6 DOF of camera position, which is further processed to place 3D graphics on the real scene …