This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target’s state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption. Recent innovations in high-speed computers, object detection techniques, and high-definition cameras, as well as advances in multisensory technology are of increasing interest to researchers in the field of video-based object tracking. Visual object tracking is an essential task in applications such as surveillance systems [1] , robotics [2] , traffic control, and human–computer interaction [3] . However, it is a challenging task due to the fact that tracked objects are always subject to deformation, various illuminations, and collisions [4] . Researchers have proposed several methods to overcome related handicaps in the field of visual tracking. Among such methods, that of the particle filter is one of the more outstanding [5] . Being able to process nonlinear and non-Gaussian systems [6] , it is widely used for object tracking and is able to handle both the gray change of an object and occlusions between objects well [7] – [11] . A particle filter is a method that recursively constructs posterior probability distributions of a state space. It is basically a Bayesian filtering method that uses the Sequential Monte Carlo method to simulate the posterior distribution of a system state. A state model representing characteristics such as position, speed, and scale should be defined in a two-dimensional image space [12] , [13] . Additionally, an observation, made from using texture, shape, and color information, is conducted to establish a likelihood model [12] , [14] , [15] . A conventional particle filter, also known as a condensation algorithm [16] , is highly robust to clutter. Although it is a robust tracker, it suffers from high computational burden and inefficient particle distribution [17] . High computational burden will not affect the accuracy of tracking but can extend a system’s capacity; thus, the default number of particles within a particle filter method needs to be reduced. However, a reduction of this kind should be made in such a way so as not to interfere with the accuracy of tracking. A particle filter is expected to perform with high accuracy under the minimum number of particles necessary. A robust tracker based on sparse representation, by combining a generative model with a discriminative model, was introduced in [18] . This method, however, failed to track under occlusion and background clutter. In [19] , authors proposed a particle filter algorithm with an adaptive noise model. In [20] , a multifeature target representation including color, shape, and local binary pattern (LBP) was used with a particle filter. It has shown some increase in accuracy; however, the associated computational load was high. In [21] , a firefly algorithm–based particle filter was introduced that achieved a robust tracking. In [17] , an iterative particle filter that increased the tracking accuracy is presented. However, it was not able to affect the computational burden. In [22] and [23] , authors proposed new architectures for a particle filter. However, these only affected the tracking speed and not the accuracy. A layered particle filter was introduced for vehicle tracking, but it gives out error in the case of occlusion [24] . Although there is a considerable number of studies on particle filter–based tracking, there is still the absence of a method having both high accuracy and low computational load. This paper proposes a novel vehicle tracking method that redistributes and filters the particles of a particle filter depending on their angular differences to a target vehicle’s direction. The conventional likelihood model is modified by the target vehicle’s motion. This approach ensures that those particles located close (in terms of the motion angle) to the target vehicle motion increase their probability in a given likelihood function (part of the process of estimation of a target’s state parameters). The primary contributions of the proposed model are as follows: particle consumption is reduced and tracking accuracy is improved due to an increase in the probabilities pertaining to the particles. Additionally, the particle distribution is more efficient, which enables our model to be more stable and robust against noise. This paper is organized as follows. In Section II, a conventional particle filter method is described. Section III introduces the proposed approach. The experimental results and considerations are presented in Section IV. Section V presents our conclusions and further study. Particle filtering was originally developed to track objects in clutter. The state of a tracked object at any given time can be denoted by a vector, say x _t , where t represents time. Similarly, vector z _t can be said to denote the observations z ₁ ,…, z _t up to time t . Particle filters are often used when the posterior density, p( x _t | z _t ), and the observation density, p( z _t | x _t ), are non-Gaussian. The key function of a particle filter is to approximate a probability distribution of a set of samples through a series of appropriate weights. Each sample, s , in the set S = {( s ⁽ⁿ⁾ , π ⁽ⁿ⁾ | n =1,…, N )} represents a hypothetical state of the object being tracked and has a corresponding discrete sampling probability, π , where The evolution of the sample set is described by propagating each sample according to a system model. Then, each element of the set is weighted in terms of the observations, and N samples are drawn, with replacement, by choosing a particular sample with probability The mean state of an object being tracked is estimated at each time step by In a particle filter, several features such as image contrast, image frame difference, edge-detected silhouette, 2D or 3D contours, gray level, and RGB or HSV color spaces are used for the tracking process. The features for tracking are selected in relation to the type of moving target and the conditions of the tracking environment [25] . Color is one of the most commonly used features for it is effective in terms of scaling variation and rotation, as well as being robust to object deformations and partial occlusions within the tracking environment. It is independent from rotation and scale, and can be easily obtained. In our experiments, color histograms for measuring the similarity (likelihood) are typically calculated in a hue saturation value (HSV) space. To make the proposed method less sensitive to illumination conditions, we used a HSV color space of 8 × 8 × 4 bins (where value (V) is represented by four bins) [26] . In a tracking approach, the estimated state of the target vehicle is updated at each time step by taking any new observations into account. Therefore, the proposed method needs a similarity measure based on the distributions of the aforementioned color histograms. One such popular measure between two given distributions, p [ u ] and q [ u ], is that of the Bhattacharyya coefficient. The associated Bhattacharya distance is defined as follows: Finally, the likelihood weights are specified by the following Gaussian distribution with variance σ _c : where N is the number of samples [27] . We use an ellipse to model our target vehicle and particles. The following 4-D state model represents a dynamic model of such an ellipse: where x and y refer to the coordinates of the center location of the ellipse, and H_x and H_y refer to the lengths of the major and minor axes of the ellipse, respectively. These are the basic steps of a conventional particle filter algorithm using a single feature. When a target vehicle is occluded by another car or object, new measurements cannot be taken. Therefore, in such cases, the likelihood function cannot be utilized and tracking can be difficult. In the proposed particle filter, the direction feature supports the tracker in cases where scalar features cannot be detected. The first step in our proposed method is to calculate the direction of motion of the target vehicle. This direction is defined in terms of an angle. A schematic diagram of a target object’s motion is shown in Fig. 1 . The target vehicle moves from location A to B, and its motion angle relative to the horizontal axis is θ _t−1 . It’s direction can be denoted in terms of a displacement vector, by using the center point of the target vehicle in the current frame, ( x_t , y_t ), and its center point from the previous frame, ( x _t−1 , y _t−1 ). This process is formulated as follows: The direction of the target may change; for example, if the target vehicle changes lanes. Therefore, the direction feature must be updated for accurate tracking. In this study, this is achieved through use of an approximate median method [11] , as follows: where H represents a parameter to be updated at times t and t + 1, respectively. The new measurement of such time steps is denoted by M . Particles are observed and weighted depending on the observations. In a conventional particle filter, particles are evenly distributed around a target. Unlike a conventional filter, the proposed filter does not use all one hundred particles for tracking. The angle between the target vehicle and each particle is calculated. The aim is to identify the angular distance of each particle to the direction of motion of the vehicle. The angular distance is used to weight the particles and renew their distribution around the vehicle. This calculation can be performed by using the distributed particles of the current time step and the vehicle’s estimated location in the previous time step. This is illustrated in Fig. 2 . In Fig. 2 , the red circles are the candidate particles at time step t . At time step t − 1, the state parameters are already estimated. These parameters include the center of the target (vehicle) ( x _t−1 , y _t−1 ). Each blue line represents the location of a particular particle with reference to the target’s position in the previous frame. These angles are used to determine the weighting coefficients of the corresponding particles. The calculation for this is given by After this calculation, the difference between the direction of motion and each value of is obtained. This is done by subtracting the results of (6) and (8). In (9), is the angle of the n th particle with reference to the direction of motion of the target vehicle; it has a range of [0, π]. The next step is to filter the particles. This filtering process is done by applying a threshold control to all the results of (9). This control assigns new weights to particles according to the results of (8).