A Prototype Model to Detect Elephants near the Railway Tracks

Most of the countries of the world are covered by dense forests. Most of the forests are the habitats of the wild animals like Elephants. In a country like India railway tracks are laid through most of the forests. The most of the railway tracks run through the elephant corridors. Absence of effective infrastructure to detect the presence of the elephants crossing the railway tracks leads to the death of these animals. Little work has been done for the proper detection of elephants. Effective automated system can be devised using seismic sensors to detect the presence of elephants crossing the railway tracks.

Work has been done in the detection of animals using acoustic, seismic, wireless sensors etc. [1-3]. Koik and Ibrahim have worked on literature survey on animal detection methods using digital images [4]. Prabhu and Kumar have performed review work on recent advances in Elephant tracking and detection systems [5]. Sharma and Shah have performed overview on different animal detection methods [6]. Prince have worked on the method of spectral energy magnitude and highest pitch frequency generated by elephants to detect them [7]. Sugumar and Jayaparvathy have performed a work on a system based on image feature extraction and migration patterns to detect the presence of elephants [8, 9]. Shaikh et al. have performed a work on image mining algorithm to detect animals [10]. Rahayani et al. have performed a work on elephant detection system using radio signals [11]. Rangdal and Hanchate have performed a work on feature extraction system using Haar of Oriented gradients [12]. Artificial Neural Networks can be used effectively to detect the patterns which indicate the presence of Elephants near the railway tracks. ANN has already been used in many applications which is related to pattern recognition [13, 14].

This paper is divided into three sections. Section I-Discusses about the architecture of the prototype model. Section II-Discusses the overall methodology of the prototype model. Section III – Discusses the Result Analysis.

Here the Layer-2 neurons generate the output as given in Eq. (1) and Eq. (2). ‘y_out¹’, denotes the output by taking input from the first ten neurons. ‘y_out²’ generates the output by taking input from the next ten neurons. The weight $w^{1 i j}$  is set to ‘1’ to indicate the presence of Elephants in the proximity of the first geophone. The weight $w^{2 i j}$  is set to ‘0.1’ to indicate the presence of Elephants in the proximity of the second geophone. The proximity of the two geophones is divided into ten concentric circles of radii 1 to 10 kms. The two weights are selected randomly and are kept fixed. The two weights are kept different in order to identify the presence of elephants in different seismic zones.

y_out¹ =$\sum_{i=1}^{10} x^{1 i} w^{1 i j}$     (1)

y_out² = $\sum_{i=1}^{10} x^{2 i} w^{2 i j}$     (2)

‘z_out’ as given in Eq. (3), denotes the output of the Layer-3 neuron. The output is fed to a function ‘f’.

z_out = y_out¹_*w^1jk + y_out²_*w^{2jk      (3)}

z = f (k_out)       (4)

f = {(v¹, v²) where v¹ = z_out div 10 && v² = z_out mod 10}       (5)

Function ‘f’ generates the values of ‘v¹’ and ‘v²’ which indicates the exact location of the elephants near the railway tracks as given in Eq. (4) and Eq. (5). The net outputs y_out¹, y_out² and z_out are based on perceptron. Function ‘f’ has been developed here to find out the location of the elephant movement. The current location of the elephants near the railway tracks is indicated by the output ‘z’ which is a vector. For example, if the value of z is (0, 0) then it indicates that no elephant is present in the proximity of any of the geophones. Value (2, 1) depicts the presence of elephants in the proximity of geophone-1 as well as geophone-2. Here v¹ is 2 and v² is 1 which indicates the presence of elephant in the proximity of geophone-1 is 8 kms and presence of elephant in the proximity of geophone-2 is 9 kms.

It is a challenging task to implement such automated systems. In most of the places manual system to drive away elephants are used. For example in some places train drivers are responsible for detecting the movement of elephants on the railway tracks. If such movements are seen they inform the nearby railway crossings where loud speakers are installed to produce bee sound to drive away elephants. Such loud speakers are not able to cover the whole stretch of the forest. And it is very difficult for the train drivers to detect the movement of elephants from the moving trains. The prototype model detects the presence of Elephants throughout the stretch. The existing methods used to detect elephants now days are either partially or fully manual but the prototype model has been designed to implement fully automated elephant detection systems. The traditional system used to detect elephants emphasizes on the presence of Elephants near Elephant corridors and takes precautionary measures accordingly in those places only. The prototype model emphasizes on any possibility of the presence of single Elephant away from the corridors. Most of the traditional methods are manual but the prototype method is fully automated. In some traditional methods electric fencing is used to restrict elephants to certain areas. The Elephants are clever enough to cross those barricades also. They use dry branches to avoid electric fences and try to cross the railway tracks but in the prototype model no such possibility is there as electric fences are not used. In traditional methods manual follow up of the herds by forest officials is required which is not required in the prototype model. Now a days in some places authority use bee sound to drive away elephants from the railway tracks. But those systems are fully manual as continuously vigilance is required to observe the movement of elephants. In the prototype model bee sound can be used to drive away elephants but the system will be fully automated and no vigilance will be required to watch the movement of the elephants.