## 1 Introduction  
In the last few decades, obesity has become a major health issue. Obesity increases the risk of many fatal diseases such a diabetes, heart attack, high cholesterol, some form of cancers (breast and colon cancer), and respiratory problems [1]. One of the main reasons for the increase in obesity is unhealthy dietary habits. Unhealthy dietary habits can be eating unhealthy foods, eating foods that contain a large amount of sugar, or just overeating. A person is considered obese when the Body Mass Index (BMI) of that person is greater than 30 kg/m\({}^{2}\)[2]. In order to maintain a healthy BMI, daily food intake should be within a prescribed limit. In other words, tacking obesity requires consumption of nutritious meals with a proper calorie intake. Therefore, it is very important to have effective means of estimating and tracking one's daily calorie consumption. Measuring approximate calories directly from the food can be a great abtement in this regard. However, to the best of our knowledge, there is no medical technology that can calculate in real time the amount of calories contained in any food. The conventional practice followed in food industry labels the calorie count of each ingredient that is used to prepare a food item. For instance, one of the largest fast food restaurant chains, McDonald's, labels the amount of calories against each ingredient within a food item [3]. This labeling is performed manually based on a calorie table suggested by the health care experts [4]. The process, however, is expensive, laborious, error-prone, time-consuming, and most importantly, it has a small impact on controlling the calorie intake of an individual. A much pragmatic solutions to this problem would be to design and develop a real-time image recognition based food calorie estimation system. This system would offer a fast and inexpensive way of calorie measurement with sublimer accuracy. For the reference, a food image recognition system is a kind of computer vision that can automatically recognize the food images based on a supervised data set. However, developing a food image based classification system is challenging due to the advent variations of images resulting from heterogeneous conditions, e.g., changing in light conditions, food shapes, and occlusions, among others [5]. Therefore, considering a suitable set of parameters is necessary when designing pattern recognition systems within the parameters of supervised learning [6]. Research on this track mainly emphasizes on recognizing the food images [7,8], with a very little focus on estimating the food calories through image recognition [9,10]. An assessment on the reported results reveals that the methods are mostly expensive in terms of time and computational complexity [11]. Moreover, the majority of the image recognition methods are inconvenient for a meal with multiple food items, and are not designed for estimating the food calories [12]. Therefore, it is important to have a food calorie estimation method that is both lightweight and optimized in relation to space and time complexity for recognizing multiple items simultaneously at a time. In this connection, computer vision-based approaches, such as Convolution Neural Networks (CNN), are proven effective as a lightweight real-time image classification method for estimating the calories from food images [13]. Taking advantage of the CNN method, this study involves the design of an automated calorie estimation system with the help of neural networks to ensure better accuracy compared with the existing methods. This system can be run on a smart device equipped with a built-in camera, making it easy to recognize food items in estimating the constituent calories by leveraging a predefined data set of daily food intake. The developed image recognition method is a soft real-time system. The user request can be processed in milliseconds to offer real-time response to the user.