An anomaly detector (106, 1200) for detecting an anomaly in a sequence of poses of a human performing an activity, comprising: an input interface (202, 1202) configured to accept input data indicative of a distribution of the sequence of poses; a memory (204, 1206) configured to store a discriminative one-class classifier (206, 1208) having a pair of complementary classifiers bounding normal distribution of pose sequences in a reproducing kernel Hilbert space, RKHS; a processor (208, 1204) configured to: embed the input data into an element of the RKHS; and classify the embedded data using the discriminative one-class classifier; and an output interface (210, 1216) configured to render a classification result; wherein the sequence of poses corresponds to a set of image frames of the human performing the activity; wherein the input data indicative of the distribution of the sequence of poses corresponds to the set of images of the human performing the activity; wherein the normal distribution of pose sequences corresponds to a distribution of normal pose sequences of poses of the human while performing the activity; wherein the processor (208, 1204) is further configured to detect an anomaly, in the sequence of poses of the human performing the activity, based on the classified embedded data; wherein the anomaly is classified from normal data, where the normal data corresponds to the normal distribution of pose sequences based on the discriminative one-class classifier (206, 1208); wherein the discriminative one-class classifier (206, 1208) detects outliers from the bounded normal distribution of pose sequences as anomaly in the human poses; wherein the discriminative one-class classifier (206, 1208) is trained based on a classification model, wherein the classification model corresponds to at least a bag-of-words model, a convolutional neural network, CNN, model, or a deep-learning model; and wherein the discriminative one-class classifier (206, 1208) is trained to estimate data density of the normal distribution of pose sequences based on a min-max optimization technique, and wherein the min-max optimization technique is utilized to: minimize distance between each classifier of the pair of complementary classifiers, and maximize margin of each classifier of the pair of complementary classifiers for maximum encapsulation of the normal distribution of pose sequences. an input interface (202, 1202) configured to accept input data indicative of a distribution of the sequence of poses; a memory (204, 1206) configured to store a discriminative one-class classifier (206, 1208) having a pair of complementary classifiers bounding normal distribution of pose sequences in a reproducing kernel Hilbert space, RKHS; a processor (208, 1204) configured to: embed the input data into an element of the RKHS; and classify the embedded data using the discriminative one-class classifier; and an output interface (210, 1216) configured to render a classification result; embed the input data into an element of the RKHS; and classify the embedded data using the discriminative one-class classifier; and an output interface (210, 1216) configured to render a classification result; wherein the sequence of poses corresponds to a set of image frames of the human performing the activity; wherein the input data indicative of the distribution of the sequence of poses corresponds to the set of images of the human performing the activity; wherein the normal distribution of pose sequences corresponds to a distribution of normal pose sequences of poses of the human while performing the activity; wherein the processor (208, 1204) is further configured to detect an anomaly, in the sequence of poses of the human performing the activity, based on the classified embedded data; wherein the anomaly is classified from normal data, where the normal data corresponds to the normal distribution of pose sequences based on the discriminative one-class classifier (206, 1208); wherein the discriminative one-class classifier (206, 1208) detects outliers from the bounded normal distribution of pose sequences as anomaly in the human poses; wherein the discriminative one-class classifier (206, 1208) is trained based on a classification model, wherein the classification model corresponds to at least a bag-of-words model, a convolutional neural network, CNN, model, or a deep-learning model; and wherein the discriminative one-class classifier (206, 1208) is trained to estimate data density of the normal distribution of pose sequences based on a min-max optimization technique, and wherein the min-max optimization technique is utilized to: minimize distance between each classifier of the pair of complementary classifiers, and maximize margin of each classifier of the pair of complementary classifiers for maximum encapsulation of the normal distribution of pose sequences. minimize distance between each classifier of the pair of complementary classifiers, and maximize margin of each classifier of the pair of complementary classifiers for maximum encapsulation of the normal distribution of pose sequences.