Bijan Shahbaz Nejad

@inproceedings{car-pose-estimation,

title = {Car Pose Estimation through Wheel Detection},

author = {Peter Roch and Bijan Shahbaz Nejad and Marcus Handte and Pedro José Marrón},

editor = {George Bebis and Vassilis Athitsos and Tong Yan and Manfred Lau and Frederick Li and Conglei Shi and Xiaoru Yuan and Christos Mousas and Gerd Bruder},

doi = {10.1007/978-3-030-90439-5_21},

isbn = {978-3-030-90439-5},

year  = {2021},

date = {2021-10-06},

urldate = {2021-10-06},

booktitle = {Advances in Visual Computing},

volume = {16},

pages = {265--277},

publisher = {Springer International Publishing},

abstract = {Car pose estimation is an essential part of different applications, including traffic surveillance, Augmented Reality (AR) guides or inductive charging assistance systems. For many systems, the accuracy of the determined pose is important. When displaying AR guides, a small estimation error can result in a different visualization, which will be directly visible to the user. Inductive charging assistance systems have to guide the driver as precise as possible, as small deviations in the alignment of the charging coils can decrease charging efficiency significantly. For accurate pose estimation, matches between image coordinates and 3d real-world points have to be determined. Since wheels are a common feature of cars, we use the wheelbase and rim radius to compute those real-world points. The matching image coordinates are obtained by three different approaches, namely the circular Hough-Transform, ellipse-detection and a neural network. To evaluate the presented algorithms, we perform different experiments: First, we compare their accuracy and time performance regarding wheel-detection in a subset of the images of The Comprehensive Cars (CompCars) dataset. Second, we capture images of a car at known positions, and run the algorithms on these images to estimate the pose of the car. Our experiments show that the neural network based approach is the best in terms of accuracy and speed. However, if training of a neural network is not feasible, both other approaches are accurate alternatives.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-030-90439-5_15,

title = {Evaluating User Interfaces for a Driver Guidance System to Support Stationary Wireless Charging of Electric Vehicles},

author = {Bijan Shahbaz Nejad and Peter Roch and Marcus Handte and Pedro José Marrón},

editor = {Bebis George

and Athitsos Vassilis

and Yan Tong

and Lau Manfred

and Li Frederick

and Shi Conglei

and Yuan Xiaoru

and Mousas Christos

and Bruder Gerd},

doi = {10.1007/978-3-030-90439-5_15},

isbn = {978-3-030-90439-5},

year  = {2021},

date = {2021-10-04},

urldate = {2021-10-04},

booktitle = {Advances in Visual Computing},

volume = {16},

pages = {183--196},

publisher = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{image-processing-pipeline-optimization,

title = {Systematic Optimization of Image Processing Pipelines Using GPUs},

author = {Peter Roch and Bijan Shahbaz Nejad and Marcus Handte and Pedro José Marrón},

editor = {George Bebis and Zhaozheng Yin and Edward Kim and Jan Bender and Kartic Subr and Bum Chul Kwon and Jian Zhao and Denis Kalkofen and George Baciu},

doi = {10.1007/978-3-030-64559-5_50},

isbn = {978-3-030-64559-5},

year  = {2020},

date = {2020-10-05},

booktitle = {Advances in Visual Computing},

volume = {15},

pages = {633--646},

publisher = {Springer International Publishing},

address = {Cham},

series = {Lecture Notes in Computer Science},

abstract = {Real-time computer vision systems require fast and efficient image processing pipelines. Experiments have shown that GPUs are highly suited for image processing operations, since many tasks can be processed in parallel. However, calling GPU-accelerated functions requires uploading the input parameters to the GPU's memory, calling the function itself, and downloading the result afterwards. In addition, since not all functions benefit from an increase in parallelism, many pipelines cannot be implemented exclusively using GPU functions. As a result, the optimization of pipelines requires a careful analysis of the achievable function speedup and the cost of copying data. In this paper, we first define a mathematical model to estimate the performance of an image processing pipeline. Thereafter, we present a number of micro-benchmarks gathered using OpenCV which we use to validate the model and which quantify the cost and benefits for different classes of functions. Our experiments show that comparing the function speedup without considering the time for copying can overestimate the achievable performance gain of GPU acceleration by a factor of two. Finally, we present a tool that analyzes the possible combinations of CPU and GPU function implementations for a given pipeline and computes the most efficient composition. By using the tool on their target hardware, developers can easily apply our model to optimize their application performance systematically.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{driver-guidance-system,

title = {A Driver Guidance System to Support the Stationary Wireless Charging of Electric Vehicles},

author = {Bijan Shahbaz Nejad and Peter Roch and Marcus Handte and Pedro José Marrón},

editor = {George Bebis and Zhaozheng Yin and Edward Kim and Jan Bender and Kartic Subr and Chul Bum Kwon and Jian Zhao and Denis Kalkofen and George Baciu},

doi = {10.1007/978-3-030-64559-5_25},

isbn = {978-3-030-64559-5},

year  = {2020},

date = {2020-10-05},

booktitle = {Advances in Visual Computing},

volume = {15},

pages = {319--331},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Air pollution is a problem in many cities. Although it is possible to mitigate this problem by replacing combustion with electric engines, at the time of writing, electric vehicles are still a rarity in European cities. Reasons for not buying an electric vehicle are not only the high purchase costs but also the uncomfortable initiation of the charging process. A more convenient alternative is wireless charging, which is enabled by integrating an induction plate into the floor and installing a charging interface at the vehicle. To maximize efficiency, the vehicle’s charging interface must be positioned accurately above the induction plate which is integrated into the floor. Since the driver cannot perceive the region below the vehicle, it is difficult to precisely align the position of the charging interface by maneuvering the vehicle. In this paper, we first discuss the requirements for driver guidance systems that help drivers to accurately position their vehicle and thus, enables them to maximize the charging efficiency.  Thereafter, we present a prototypical implementation of such a system. To minimize the deployment cost for charging station operators, our prototype uses an inexpensive off-the-shelf camera system to localize the vehicles that are approaching the station. To simplify the retrofitting of existing vehicles, the prototype uses a smartphone app to generate navigation visualizations. To validate the approach, we present experiments indicating that, despite its low cost, the prototype can technically achieve the necessary precision.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}