UJJWAL , Ujjwal

The main objective of this thesis is to improve the detection performance of deep learning based pedestrian detection systems without sacrificing detection speed. Detection speed and accuracy are traditionally known to be at trade-off with one another. Thus, this thesis aims to handle this trade-off in a way that amounts to faster and better...

We propose a unified network for simultaneous detection and tracking. Instead of basing the tracking framework on object detections, we focus our work directly on tracklet detection whilst obtaining object detection. We take advantage of the spatio-temporal information and features from 3D CNN networks and output a series of bounding boxes and...

Pedestrian detection is a specific instance of the more general problem of object detection in computer vision. A balance between detection accuracy and speed is a desirable trait for pedestrian detection systems in many applications such as self-driving cars. In this paper, we follow the wisdom of " and less is often more" to achieve this...

Achieving high detection accuracy and high inference speed is important for a pedestrian detection system in self-driving applications. There exists a trade-off between detection accuracy and inference speed in modern convolu-tional object detectors. In this paper, we propose a novel pedestrian detection system, which leverages spatial...

We propose a system design for pedestrian detection by leveraging the power of multiple convolutional layers explicitly. We quantify the effect of different convolutional layers on the detection of pedestrians of varying scales and occlusion level. We show that earlier convolutional layers are better at handling small-scale and partially...