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All Journal Mechatronics, Electrical Power, and Vehicular Technology Proceeding of the Electrical Engineering Computer Science and Informatics
Widyawardana Adiprawita, Widyawardana
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation Cahyawijaya, Samuel; Wilie, Bryan; Adiprawita, Widyawardana
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1101.071 KB) | DOI: 10.11591/eecsi.v5.1672

Abstract

In crowd counting task, our goals are to estimate density map and count of people from the given crowd image. From our analysis, there are two major problems that need to be solved in the crowd counting task, which are scale invariant problem and inhomogeneous density problem. Many methods have been developed to tackle these problems by designing a dense aware model, scale adaptive model, etc. Our approach is derived from scale invariant problem and inhomogeneous density problem and we propose a dense aware inception based neural network in order to tackle both problems. We introduce our novel inception based crowd counting model called Inception Dense Estimator network (IDEnet). Our IDEnet is divided into 2 modules, which are Inception Dense Block (IDB) and Dense Evaluator Unit (DEU). Some variations of IDEnet are evaluated and analysed in order to find out the best model. We evaluate our best model on UCF50 and ShanghaiTech dataset. Our IDEnet outperforms the current state-of-the-art method in ShanghaiTech part B dataset. We conclude our work with 6 key conclusions based on our experiments and error analysis.
CountNet: End to End Deep Learning for Crowd Counting Wilie, Bryan; Cahyawijaya, Samuel; Adiprawita, Widyawardana
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.729 KB) | DOI: 10.11591/eecsi.v5.1704

Abstract

We approach crowd counting problem as a complex end to end deep learning process that needs both a correct recognition and counting. This paper redefines the crowd counting process to be a counting process, rather than just a recognition process as previously defined. Xception Network is used in the CountNet and layered again with fully connected layers. The Xception Network pre-trained parameter is used as transfer learning to be trained again with the fully connected layers. CountNet then achieved a better crowd counting performance by training it with augmented dataset that robust to scale and slice variations.
Obstacle Avoidance Method for a Group of Humanoids Inspired by Social Force Model Sadiyoko, Ali; Trilaksono, Bambang Riyanto; Mutijarsa, Kusprasapta; Adiprawita, Widyawardana
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 2 (2015)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (873.965 KB) | DOI: 10.14203/j.mev.2015.v6.67-74

Abstract

This paper presents a new formulation for obstacle and collision behavior on a group of humanoid robots that adopts walking behavior of pedestrian crowd. A pedestrian receives position information from the other pedestrians, calculate his movement and then continuing his objective. This capability is defined as socio-dynamic capability of a pedestrian. Pedestrian’s walking behavior in a crowd is an example of a sociodynamics system and known as Social Force Model (SFM). This research is trying to implement the avoidance terms in SFM into robot’s behavior. The aim of the integration of SFM into robot’s behavior is to increase robot’s ability to maintain its safety by avoiding the obstacles and collision with the other robots. The attractive feature of the proposed algorithm is the fact that the behavior of the humanoids will imitate the human’s behavior while avoiding the obstacle. The proposed algorithm combines formation control using Consensus Algorithm (CA) with collision and obstacle avoidance technique using SFM. Simulation and experiment results show the effectiveness of the proposed algorithm.
A Hierarchical Description-based Video Monitoring System for Elderly Nari, Mochamad Irwan; Setiawan, Agung Wahyu; Adiprawita, Widyawardana
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.686 KB) | DOI: 10.11591/eecsi.v4.1044

Abstract

The increase in the number of elderly motivates academic researchers to develop technologies that can ensure self- sufficiency in their lives. In this research, prototype of an inexpensive video monitoring system for the elderly using a single RGB camera proposed. In the process is divided into two, namely vision and event recognition module. For event recognition, we use a hierarchical description-based approach with three attributes, namely posture (e.g., stand, sit and lie), location (e.g., walking zone, relaxing zone and toilet zone) and duration (e.g., short and long). Output this system is description activity recognized in the text. The experiment result shows our system can provide the effectiveness of the context description.
Co-Authors Ali Sadiyoko Bambang Riyanto Trilaksono Cahyawijaya, Samuel Cahyawijaya, Samuel Kusprasapta Mutijarsa Nari, Mochamad Irwan Setiawan, Agung Wahyu Wilie, Bryan Wilie, Bryan