Interpolation-Based Object Detection Using Motion Vectors for Embedded Real-time Tracking Systems

2018 ◽  
Author(s):  
Takayuki Ujiie ◽  
Masayuki Hiromoto ◽  
Takashi Sato
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Tracking Systems ◽  
Motion Vectors ◽  
Real Time Tracking

Prototyping real-time tracking systems on mobile devices

2016 ◽  
Author(s):  
Kyunghun Lee ◽  
Haifa Ben Salem ◽  
Thyagaraju Damarla ◽  
Walter Stechele ◽  
Shuvra S. Bhattacharyya
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Tracking Systems ◽  
Real Time Tracking

scholarly journals Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3371 ◽  
Author(s):  
Hossain ◽  
Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Moving Objects ◽  
State Of The Art ◽  
Target Position ◽  
Guidance System ◽  
Aerial Imagery ◽  
Detection And Tracking ◽  
Real Time Tracking

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

scholarly journals MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

2021 ◽  
Vol 13 (9) ◽  
pp. 1670
Author(s):  
Danilo Avola ◽  
Luigi Cinque ◽  
Anxhelo Diko ◽  
Alessio Fagioli ◽  
Gian Luca Foresti ◽  
...  
Keyword(s):  
Image Analysis ◽  
Object Detection ◽  
Real Time ◽  
Video Sequences ◽  
Multi Scale ◽  
Video Frames ◽  
Common Strategy ◽  
Real Time Tracking ◽  
Uav Images ◽  
Sort Algorithm

Tracking objects across multiple video frames is a challenging task due to several difficult issues such as occlusions, background clutter, lighting as well as object and camera view-point variations, which directly affect the object detection. These aspects are even more emphasized when analyzing unmanned aerial vehicles (UAV) based images, where the vehicle movement can also impact the image quality. A common strategy employed to address these issues is to analyze the input images at different scales to obtain as much information as possible to correctly detect and track the objects across video sequences. Following this rationale, in this paper, we introduce a simple yet effective novel multi-stream (MS) architecture, where different kernel sizes are applied to each stream to simulate a multi-scale image analysis. The proposed architecture is then used as backbone for the well-known Faster-R-CNN pipeline, defining a MS-Faster R-CNN object detector that consistently detects objects in video sequences. Subsequently, this detector is jointly used with the Simple Online and Real-time Tracking with a Deep Association Metric (Deep SORT) algorithm to achieve real-time tracking capabilities on UAV images. To assess the presented architecture, extensive experiments were performed on the UMCD, UAVDT, UAV20L, and UAV123 datasets. The presented pipeline achieved state-of-the-art performance, confirming that the proposed multi-stream method can correctly emulate the robust multi-scale image analysis paradigm.

Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

2011 ◽  
Vol 403-408 ◽  
pp. 4968-4973
Author(s):  
Rajendra Kachhava ◽  
Vivek Srivastava ◽  
Rajkumar Jain ◽  
Ekta Chaturvedi
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Real Time ◽  
Video Surveillance ◽  
Research Field ◽  
Security System ◽  
Multiple Cameras ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Real Time Tracking

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

Ghost marker detection and elimination in marker-based optical tracking systems for real-time tracking in stereotactic body radiotherapy

2014 ◽  
Vol 41 (10) ◽  
pp. 101713 ◽  
Author(s):  
Guanghua Yan ◽  
Jonathan Li ◽  
Yin Huang ◽  
Kathryn Mittauer ◽  
Bo Lu ◽  
...  
Keyword(s):  
Real Time ◽  
Stereotactic Body Radiotherapy ◽  
Optical Tracking ◽  
Tracking Systems ◽  
Real Time Tracking ◽  
Marker Detection

An Overview of Data Aggregation Architecture for Real-Time Tracking with Sensor Networks

2006 ◽  
Author(s):  
Tian He ◽  
Lin Gu ◽  
Liqian Luo ◽  
Ting Yan ◽  
John A. Stankovic ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Data Aggregation ◽  
Real Time Tracking

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

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