Hello, I am


PhD researcher @ IPI-UGent

About me

An enthusiastic computer vision researcher

Looking to pursue my career in the challenging field of computer vision, where I can use and improve my skill set to contribute to the scientific world.

My Expertise

Computer Vision

Image processing combined with machine learning.

Sensor Fusion

Multi-modality sensor fusion.

Machine Learning

Deep learning and traditional machine learning methods.

My Resume


2018 - Present

Early Stage Researcher
IPI research group, Ghent University, Belgium

  • Focused on leveraging AI and sensor fusion techniques to assist systems for better pedestrian detection & tracking.
  • Engineering and implementing deep neural networks for object detection and tracking in thermal and color images.
  • Applying techniques in machine learning, image processing and transfer learning to achieve high-performance.
  • Supervising research projects and conducting labs for master and bachelor programs.

2008 - 2018

Lecturer(2008-2016) | Asst. Prof.(2016-2018)
Computer Engneering dept., QUEST, Pakistan

  • Supervision of research and engineering projects for master and bachelor programs.
  • Conducted lectures of several bachelor courses.

2007 - 2008

Technical Support Executive
eWorld ISP, Pakistan.

  • Technical support towards clients.
  • Network troubleshooting and maintenance


2018 - Present

Ph.D. Computer Vision
IPI research group, Ghent University, Belgium

Testing, optimizing, and developing different techniques of sensor fusion, image processing, and machine learning to assist systems for better pedestrian detection in color and thermal images.

2013 - 2015

M.E. Computer Systems Engineering
Computer Engneering dept., QUEST, Pakistan

The degree was divided into four terms, from which three terms were based on course work consisting of core subjects of computer systems engineering and final term for thesis project.
Thesis: Automatic Number Plate Detection & Recognition System

2003 - 2007

B.E. Computer Systems Engineering
Computer Engneering dept., QUEST, Pakistan

The undergraduate program consists of eight semesters including fourty technical subjects related with the field of computer systems engineering and undergraduate thesis project.
Thesis: IPv6 Based WLAN


Computer Vision
Sensor Fusion
Machine Learning
Image Processing
Data Analysis
SoC (Arduino/Xilinx)
Platforms (Linux/Windows)
Web development & databases



Selected publications

Probabilistic model for sensor fusion and data association in pedestrian tracking

(under review process)

Abstract: Pedestrian detection and tracking have been the focus of interest due to their high demand and applications. A plethora of work has been done in this field in the last decades; however, due to the dynamic environments, the data obtained from a single visual sensor for these operations cannot suffice to cover all the environmental conditions. Hence, the use of multiple, heterogeneous visual sensors in such applications is indispensable. This research is focused on pedestrian detection and tracking using sequential importance resampling particle filters based on the fused data from thermal and colour images condition on environmental conditions that affect luminance change in both modalities. The proposed method uses Naive Bayes and Bayesian inference probabilistic models for late-fusion and tracking of pedestrians in the sequences. The results in this paper demonstrate that the fused data for pedestrian detection and tracking outperforms other existing state-of-the-art solutions, especially when the environmental information is taken into the account in combination with RGB-T (Red Green Blue – Thermal) sensor data.

Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images

MDPI Sensors, vol. 22, no. 22, 2022

Abstract: Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.

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Automatic annotation of pedestrians in thermal images using b-f segmentation for training deep neural networks

IEEE Symposium Series on Computational Intelligence, Australia, 2020

Abstract: Deep Neural Networks for object detection have become of significant interest with the substantial improvement in their efficiency and increase in their applications. However, training such a network requires a large annotated dataset, which is very expensive in term of time and human effort. In this context, several automatic image annotation solutions have been proposed which are dependent on rich visual features and thus suitable for color images only. On the other hand, recent experiments have proven that color cameras alone are not enough for pedestrian related applications and there is a need to use other visual sensors as well, such as thermal cameras. In this paper, we propose an automatic image annotation technique for pedestrian detection in thermal images using an adaptive background/foreground estimation model to train Faster-RCNN. The results presented in this paper, obtained through long-term experiments demonstrate the efficacy of our technique. The results also show that our proposed technique is be very useful to generate image annotations automatically and to train a deep neural network without having a manually annotated dataset for cameras with different modalities.

Read more
View all publications


Course Material

Data structures and Algorithms Lab (Algoritmen en datastructuren Labo)

Engineering Project (Ingenieursproject)

Embedded Systems: Algorithms Lab (Ingebedde Systemen: Algoritmes Labo)

Visual Programming (WPF using c#)

Computer Systems & Programming

Operating Systems

Object Oriented Programming

Microprocessor Systems

Web Engineering

Short Courses

Feel free to contact me

Phone: +32 497 190700
Address: Office #, Technicum Block-3, Ghent University, Ghent, Belgium.
Email: zuhaib.ahmed@ugent.be
Office Hours: Monday to Friday - 09:00AM to 05:00PM (CET)